时间序列数据挖掘相似性度量和周期模式挖掘研究
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摘要
随着信息技术的飞速发展,数据挖掘受到越来越多的关注。时序数据在现实生活中广泛存在,如金融市场、工业过程、科学试验、医疗、气象、水文、生物信息等,而且存储规模呈现爆炸式增长。因此对时间序列数据挖掘问题进行深入研究是非常必要和富有挑战性的。目前的时间序列数据挖掘技术尚处于起步阶段,挖掘算法有待扩充和完善。
本文在综述了时间序列数据挖掘研究发展概况后,对目前的主要方法进行了总结评述,在重新描述、相似性比较和周期模式挖掘几个方面进行了深入研究。最后在总结全文的基础上,指出了本文有待深入研究的若干问题。本文的创新性工作主要包括以下内容:
1)提出了基于形态的时间序列相似性度量方法。本方法在时间序列分段线性化的基础上,采用了基于斜率相对变化的符号化重新描述方法,可以有效描述序列形态的动态变化趋势;同时提出了一个与之对应的距离度量公式,克服了点距离度量中存在的对各种扰动敏感的缺陷。实验证明,本方法还具有时间多分辨率特征,可以比较在不同时间分辨率下的时间序列的相似程度。
2)提出了局部分段动态时间扭曲算法。经典动态时间扭曲算法(DTW)在时间序列相似性度量中具有重要作用,但由于计算复杂度较高,很难应用于实际数据库中。本文提出了一个新的算法——局部分段动态时间扭曲算法。在对时间序列进行分段线性化的基础上,将每一个段视为一个整体,应用经典的动态时间扭曲算法,通过设置补偿系数,保证了算法的精度。实验表明,本算法能够在计算精度几乎没有损失的情况下,有效地提高经典DTW算法的效率。
3)提出了一种高效的时间序列异步周期1-模式挖掘算法。本算法设计了一种基于2进制编码的映射算法,并提出了改进的点乘算法,可以通过一次计算发现一个事件在序列中出现的所有位置;并且,本算法用并行计算替代了原算法中的串行计算方法,显著减少了数据的运算和存储次数。实验证明,本算法在完全不降低原算法准确性的基础上,显著提高了算法效率。
4)首次提出了时间序列局部周期频繁模式的概念及其挖掘算法。不同于现有的所有周期挖掘算法,本算法不但能够挖掘出贯穿时间序列全局的频繁发生的周期模式,而且能够发现只在某个局部频繁发生的周期模式。本算法首先将时间序列划分为局部集合,然后基于数据自行找出序列中隐藏的潜在周期,生成局部周期频繁1-模式,最后在每一个有交叉的局部上,应用最大命中子模式树算法合成复杂模式输出。实验证明,本算法可以有效地发现时间序列中的局部周期频繁模式,其中的剪切算法和周期阈值公式能够有效提高算法效率。
Data Mining has attracted much attention with the development of information technology. Time series data are a kind of important data existing in a lot of fields, such as financial market, industrial process, science experiments, etc, and the quantity of time series data has explosively increas. So it is necessary to study on the subject of the time series data mining. Nowadays, time series mining technology is still in its infancy and the algorithms are expected to be extended and to be complemented.
After major issues in time series data mining surveyed, some algorithms are summarized and appraised in this dissertation. Then the problems on time series' representation, similarity measure and periodic pattern mining are deeply researched. At last, on the basis of the summery of the whole dissertation, we propose the several problems needed to be further researched in the future. The main innovative achievements are described as follows.
1) A novel method of shape-based time series similarity measure is proposed. Based on the PLR algorithm, the time series is represented by using the relative change of the slope of the lines, which effectively reflects the degree of the dynamic change of the tendency of the curves. Moreover a corresponding distance measure formula is proposed, which can increase the robust to disturbances compared with the point-to-point Euclidean distance measure. The experimental results show that the approach can effectively measure the similarity of time series under various analyzing frequency.
2) A novel method of local segmented dynamic time warping distance measure (LSDTW) for time series data mining is proposed. DTW is of great importance in time series similarity measure, but its expensive computation limits its application in massive datasets. The LSDTW algorithm is proposed to solve this problem. Based on the PLR algorithm, regarding every segment as a whole, classical DTW algorithm is used, and a compensate coefficient is proposed to ensure the accuracy of the method. The experimental results show that the approach gives better computational efficiency compared with the classical DTW without loss of accuracy.
3) A highly efficient of asynchronous periodic 1-patterns mining method is proposed. A binary representation based mapping scheme is designed, and a modified dot product algorithm is proposed to find all the positions of an event in the time series; and a parallel calculation method is proposed to replace the series calculation method, which notably decrease the times of the calculation and access process. The experimental results show that the approach significantly increases the efficiency without loss of the accuracy.
4) A novel concept and its mining method of local periodic frequent patterns in time series are proposed. Different from all exiting algorithms, this method not only can mine the frequent periodic patterns arised through the whole time series, but also can find the periodic patterns happened just in the locality of the sequence. This method first divides the time series into a set of fragments, then finds the hidden potential periodicities based on the data, and produces the local periodic frequent 1-pattern, at last using the max-subpattern tree method merges and outputs complex local periodic frequent patterns. The experimental results show that the approach can effectivly mine the local periodic frequent patterns in time series, and the cut algorithm and the periodicity threshold formula proposed in this paper can significantly increase the efficiency.
本文在综述了时间序列数据挖掘研究发展概况后,对目前的主要方法进行了总结评述,在重新描述、相似性比较和周期模式挖掘几个方面进行了深入研究。最后在总结全文的基础上,指出了本文有待深入研究的若干问题。本文的创新性工作主要包括以下内容:
1)提出了基于形态的时间序列相似性度量方法。本方法在时间序列分段线性化的基础上,采用了基于斜率相对变化的符号化重新描述方法,可以有效描述序列形态的动态变化趋势;同时提出了一个与之对应的距离度量公式,克服了点距离度量中存在的对各种扰动敏感的缺陷。实验证明,本方法还具有时间多分辨率特征,可以比较在不同时间分辨率下的时间序列的相似程度。
2)提出了局部分段动态时间扭曲算法。经典动态时间扭曲算法(DTW)在时间序列相似性度量中具有重要作用,但由于计算复杂度较高,很难应用于实际数据库中。本文提出了一个新的算法——局部分段动态时间扭曲算法。在对时间序列进行分段线性化的基础上,将每一个段视为一个整体,应用经典的动态时间扭曲算法,通过设置补偿系数,保证了算法的精度。实验表明,本算法能够在计算精度几乎没有损失的情况下,有效地提高经典DTW算法的效率。
3)提出了一种高效的时间序列异步周期1-模式挖掘算法。本算法设计了一种基于2进制编码的映射算法,并提出了改进的点乘算法,可以通过一次计算发现一个事件在序列中出现的所有位置;并且,本算法用并行计算替代了原算法中的串行计算方法,显著减少了数据的运算和存储次数。实验证明,本算法在完全不降低原算法准确性的基础上,显著提高了算法效率。
4)首次提出了时间序列局部周期频繁模式的概念及其挖掘算法。不同于现有的所有周期挖掘算法,本算法不但能够挖掘出贯穿时间序列全局的频繁发生的周期模式,而且能够发现只在某个局部频繁发生的周期模式。本算法首先将时间序列划分为局部集合,然后基于数据自行找出序列中隐藏的潜在周期,生成局部周期频繁1-模式,最后在每一个有交叉的局部上,应用最大命中子模式树算法合成复杂模式输出。实验证明,本算法可以有效地发现时间序列中的局部周期频繁模式,其中的剪切算法和周期阈值公式能够有效提高算法效率。
Data Mining has attracted much attention with the development of information technology. Time series data are a kind of important data existing in a lot of fields, such as financial market, industrial process, science experiments, etc, and the quantity of time series data has explosively increas. So it is necessary to study on the subject of the time series data mining. Nowadays, time series mining technology is still in its infancy and the algorithms are expected to be extended and to be complemented.
After major issues in time series data mining surveyed, some algorithms are summarized and appraised in this dissertation. Then the problems on time series' representation, similarity measure and periodic pattern mining are deeply researched. At last, on the basis of the summery of the whole dissertation, we propose the several problems needed to be further researched in the future. The main innovative achievements are described as follows.
1) A novel method of shape-based time series similarity measure is proposed. Based on the PLR algorithm, the time series is represented by using the relative change of the slope of the lines, which effectively reflects the degree of the dynamic change of the tendency of the curves. Moreover a corresponding distance measure formula is proposed, which can increase the robust to disturbances compared with the point-to-point Euclidean distance measure. The experimental results show that the approach can effectively measure the similarity of time series under various analyzing frequency.
2) A novel method of local segmented dynamic time warping distance measure (LSDTW) for time series data mining is proposed. DTW is of great importance in time series similarity measure, but its expensive computation limits its application in massive datasets. The LSDTW algorithm is proposed to solve this problem. Based on the PLR algorithm, regarding every segment as a whole, classical DTW algorithm is used, and a compensate coefficient is proposed to ensure the accuracy of the method. The experimental results show that the approach gives better computational efficiency compared with the classical DTW without loss of accuracy.
3) A highly efficient of asynchronous periodic 1-patterns mining method is proposed. A binary representation based mapping scheme is designed, and a modified dot product algorithm is proposed to find all the positions of an event in the time series; and a parallel calculation method is proposed to replace the series calculation method, which notably decrease the times of the calculation and access process. The experimental results show that the approach significantly increases the efficiency without loss of the accuracy.
4) A novel concept and its mining method of local periodic frequent patterns in time series are proposed. Different from all exiting algorithms, this method not only can mine the frequent periodic patterns arised through the whole time series, but also can find the periodic patterns happened just in the locality of the sequence. This method first divides the time series into a set of fragments, then finds the hidden potential periodicities based on the data, and produces the local periodic frequent 1-pattern, at last using the max-subpattern tree method merges and outputs complex local periodic frequent patterns. The experimental results show that the approach can effectivly mine the local periodic frequent patterns in time series, and the cut algorithm and the periodicity threshold formula proposed in this paper can significantly increase the efficiency.
引文
[1]朱明,数据挖掘,合肥:中国科技大学出版社,2002,35～36
[2]曲文龙,复杂时间序列知识发现模型与算法研究,[博士学位论文],北京;北京科技大学,2006
[3] J Han and M Kamber, Data Mining: Concepts& Techniques, San Fransisco, Morgan Kaufmann Publishers, 2001, 30～33
[4]安鸿志,陈兆国,杜金观等,时间序列的分析与应用,北京:科学出版社,1983, 8
[5]肖辉,时间序列的相似性查询与异常检测,[博士学位论文],上海:复旦大学,2005
[6] Tufte E, The visual display of quantitative information, Cheshire, Connecticut: Graphics Press,1983,92～95
[7] Funkhouser H G,historical development of the graphical representation of statistical data, The American Mathematical Monthly, 1938, 45(8): 541～542
[8] Chen M S, Han J, Yu P S, Data mining: An overview form a database perspective, IEEE Transactions on Knowledeg and Data Engineering, 1996,8(6):866～883
[9] Julian Kulkarn, Richark King, Business intelligence systems and data mining, ASAS Institute White Paper,1996,68～72
[10] Fayyad U, Piatetsky S G, Smyth P, Knowledge discovery and data mining towards a unifying framework, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Oregon, Portland, 1996, 82～88
[11] Piatetsky S G, Fayyad U, Smith P, From data mining to knowledge discovery in database, AI Magazine, 1996, 17: 37～54
[12] Beckman N N, Kriegel H P, Seager B, The R-tree: An efficient and robust access method for point and rectangles, Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, Atlantic, U.S.A.,1990, 322～331
[13] Berch T, Keim D, Kriegel H P, The X-tree: an index structure for high dimensional data, Proceedings of the 22nd International Conference on Very Large Databases, San Francisco,U.S.A.,1996, 28～39
[14] Ester M, Hans-Peter K, Sander J, Knowledge discovery in spatial database, Institute for Computer Science, University of Munich, Oetingenstr, 2000,35～38
[15] KoperskiK,Han J, Discovery of spatial association rules in geographic information databases, Proceedings of the 4th International Symposium on Advances in Spatial Databases, Portland Maine, 1995, 47～66
[16] Zaiane O R, Han J, Zhu H, Mining recurrent items in multimedia with progressive resolution refinement, Proceedings of International Conference on Data Engineering, California, U.S.A., 2000, 2,461～470
[17] Keogh E J, Pazzani M J, An indexing scheme for fast similarity search in large time series databases, Proceedings of 11th International Conference on Scientific and Statistical Database Management, Ohio, U.S.A.,1999, 56～67
[18] Han J, Dong G, Yin Y, Efficient mining partial periodic paterns in time series database, Proceedings of the 15th International Conference on Data Enginieering, Sydney, Australia, 1999, 106～115
[19] GhaniR,Slattery S, Yang Y, Hypertext categorization using hyperlink paterns and meta data, Proceedings of 18th International Conference on Machine Learning, San Francisco, U.S.A., 2001,178～185
[20] Joachims T, Text categorization with support vector machines: Learning with many relevant features, Proceedings of 10th European Conference on Machine Learning, Chemnitz,Germany, 1998, 137～142
[21] Sullivan T, Reading reader reaction: A proposal for inferential analysis of web server log files, Proceedings of the 3rd Human Factors and the Web Conference, Colorado, U.S.A,1997,216～223
[22] Srivastava J, Cooley R, Deshpande M, Web usage mining: Discovery and application of usage paterns from web data, SIG KDD Explorations, 2000, 1(2): 12～23
[23] Fayyad U M, Data Mining and knowledge discovery: Making sense out of data,IEEE Expert,1996,11(5):20～25
[24] Mitra S, Pal S K, Mitra P, Data mining in soft computing framework: A Survey, IEEE Transactions on Neural Networks, 2002,13(1): 3～14
[25] Chen M-S, Han J, Yu P S, Data mining: An overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 866～883
[26]袁增任,人工神经元网络及其应用,北京:清华大学出版社,2000,124～130
[27] J P Bigus, Data mining with neural networks, New York: Mc Graw-Hill, 1996,56～68
[28] Qualian JR, Induction of decision trees, Machine Learning, 1986, 1:81～106
[29]李敏强等,遗传算法的基本理论与应用,北京:科学出版社,2002,1～425
[30]曾黄麟,粗集理论及其应用,重庆:重庆大学出版社,1996,29～59
[31] (美)L A扎德,陈国权译,模糊集合、语言变量及模糊逻辑,北京:科学出版社,1982,1～199
[32] W Cleveland,Visualizing data summit, NJ: Hobart Press, 1993,97～125
[33] J L Devore, Probability and statistics for engineering and sciences, New York: Duxbury Press, 1995,68～72
[34] Agrawal R,Shafer J C, Parallel mining of association rules, IEEE Transactions on Knowledge and Data Engineering,1996, 8(6): 962～969
[35] Park J S, Chen M S, Yu P S, et al., Using a hash-based method with transaction trimming for mining association rules, IEEE Transactions on Knowledge and Data Engineering, 1997, 9(5): 813～825
[36] Srikant R, Agrawal R, Mining Sequential Paterns: Generalization and performance improvement, Proceedings of the International Conference on Extending Database Technology,Avignon, France, 1996,3～17
[37] Wang K, Discovering patterns from large and dynamic sequential data, Journal of Intelligent Information Systems, 1997, 9(1): 8～33
[38] Zhang T, Ramakrishnan R, Livny M, et al., BIRCH: An efficient data clustering method for very large databases, In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996,103～114
[39] Hinneburg A, Keim D A, An eficient approach to clustering in large multimedia databases with noise, Proceedings 4th Int. Conference on Knowledge Discovery in Databases (KDD'98), New York, U.S.A., September, 1998, 58～65
[40] Quinlan, J R, Induction of decision trees, Machine Learning, 1986,1(1): 81～106
[41] Breiman L, Friedman J H, Olshen R A, et al., Classification and regression trees, Wadsworth, Belmont, 1984, 6～26
[42] Arning, A, Agrawal R, Raghavan P, A linear method for deviation detection in large databases, Proceedings 2nd Int. Conference on Knowledge Discovery and Data Mining, Portland, 1996, 164～169
[43] Knorr E M, Ng R T, and Zamar R H, Robust space transformations for distance-based operations, KDD-01, San Francisco, U.S.A., 2001, 126～135
[44]王晓晔,时间序列数据分类方法的研究:【博士学位论文],天津;天津大学,2003
[45]欧阳为民,蔡庆生,数据库中的时态数据发掘研究,计算机科学,1998,25(4): 60～63
[46] Keogh E J, Pazzani M I, An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback, Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, Menlo Park,U.S.A.,1998, 239～243
[47] Box G E, Jenkins G M, Reinsel G C,时间序列分析:预测与控制,北京:中国统计出版社,1997,125～131
[48] T Bozkaya, N Yazdani, Z M Ozsoyoglu, Matching and indexing sequences of different lengths, In Proceeding of the 6th Int Conference on Information and Knowledge Management, Las Vegas, U.S.A., 1997, 128～135
[49] Y Huang and P S. Yu, Adaptivc query processing for time-series data, In Proceedings of the 5th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, San Dicgo, U.S.A., 1999, 282～286
[50] K Kalpakis, D Gada, V Puttagunta, Distance measure for effectivc clustering of ARIMA time series, in Proceedings of the 1st IEEE Int Conf on Data Mining, San Jose, U.S.A., 2001,273～280
[51] Wai-Ho Au, Keith K, C Chan, Mining fuzzy rules for time series classification , Budapest, Hungary,July, 2004, 25～29
[52] E Keogh and S Kasctty, On the Need for Time Series Data Mining Benchmarks: A Survey and Empincal Demonstration, Data Mining and Knowledge Discovery, 2003(7): 349～371
[53] Harms, S K, Deogun J, Tadesse T, Discovering sequential association rules with contraints and time lags in multiple sequences, In proceedings of the 13th Int’l Symposium on Methodologies for Intelligent Systems, Lyon,France, 2002, 432～441
[54] Jin X, Lu Y, Shi C, Distribution discovery: Local analysis of temporal rules,In proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery and Mining, Taipei,Taiwan, 2002, 469～480
[55] Li C, Yu P, Castelli V, MALM: A framework for mining sequence database at multiple abstraction levels, Proceedings of the 7th International Conference on Information and Knowledge Management, Washington , U.S.A., November 3-7,1998,267～272
[56] Radhakrishnan N, Wilson J, Loizou D, An alternate partitioning technique to quantify the regularity of complex time series, International Journal of Bifurcation and Chaos, 2000, 10(7):1773～1779
[57] Cotofrei, P, Statistical temporal rules, In proceedings of the 15th Conference on Computational Statistics - Short Communications and Posters, Berlin, Germany, August, 2002,24～28
[58] Cotofrei P, Stoffel K, Classification rules+time= temporal rules, In proceedings of the 2002 Int’l Conference on Computational Science, Amsterdan, Netherlands, April, 2002,572～581
[59] Schittenkopf C, Tino P, Dorffner G, The benefit of information reduction for trading strategies, Report Series for Adaptive Information Systems and Management in Economics and Management Science, July, 2000, Report #45
[60] Tino P, Schittenkopf C, Dorffner G, Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams: Lessons learned from financial volatility trading, Report Series for Adaptive Information Systems and Management in Economics and Management Science, July, 2000, Report #46
[61] Yairi, T, Kato, Y, Hori, K,Fault detection by mining association rules in house-keeping data, In Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Montreal, Canada, June, 2001, 18～21
[62] Keogh E,Lin J,Trupple W, Clustering of time series subsequences is meaningless:Implications for past and future research, Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, Australia, 2003,115～122
[63] J Takeuchi, K Yamanishi, A unifying framework for detecting outliers and change points from time series, IEEE Transanctions on Knowledge and Data Engineering, 2006, 18(4):482～492
[64] Jagadish H V, Koudas Nick and Muthukrishnan S, Mining deviants in a time series database, Proceedings of 25th International Conference on Very UK Data Bases(VLDB'99), Scotland,UK,September,1999, 7～10
[65] Kotsakis E, and Wolski A, MAPS: A method for identifying and predicting aberrant behaviour in time series, Proceedings 14th Internet Conferenceerenceon Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-2001), Budapest, Hungary, June, 2001, 4～7
[66] Keogh E, Lonardi, S and Chiu W, Finding surprising paterns in a time series database in linear time and space,In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, U.S.A., July, 2002,550～556
[67] Shahabi C,Tian X and Zhao W, Tsa-tree: A wavelet_based approach to improve the efficiency of multi-level surprise and trend queries,In Proceedings 12th International Conference on Scientific and Statistical Database Management, Berlin, Germany, 2000,55～68
[68] R Agrawal, T Imielinski, A Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD'93),Washington, 1993,207～216
[69] Agrawal R, Srikant, Mining sequential patterns, In Proceeding 95 Int'l Conference Data Engineering, Taibei, Taiwan, March, 5,1995, 3～14
[70] Loether H J, McTavish D G, Descriptive and inferential statistics: An introduction, Boston: Allyn and Bacon, 1993,85～96
[71] R Agrawal, C Faloutsos, and A Swami, Eficient similarity search in sequence databases, In Int. Conference on Foundations of Data Organization (FODO),1993:69～84
[72] R Agrawal, K I Lin,H S Sawhney,et a1., Fast similarity search in the presence of noise, scaling, and translation in time-series databases, Proceeding 21st Int'l Conference Very Large Data Bases(VLDB'95), 1995, 490～501
[73] X Ge and P Smyth, Deformable markov model templates for time series pattern matching, In Proceeding ACM SIGKDD Int. Conference Knowledge Discovery Data Mining, 2000, 81～90
[74]刘世元,江浩,面向相似搜索的时间序列表示方法评述,计算机工程与应用,2004, 27:53～59
[75] F K Chan, A W Fu, C Yu, Haar wavelets for efficient similarity search of time-series: With and without time warping,Knowledge and Data Engineering, IEEE Transactions on, 2003,15 (3): 686～705
[76] T Warren Liao,Clustering of time series data- a survey,2005 Pattern Recognition Society, Pattern Recognition,2005,38 (11):1857～1874
[77] S Policker, A B Geva, Nonstationary time series analysis by temporal clustering, IEEE Transactions on Systems Man Cybernetics, 2000, 30(2): 339～343
[78] M Vlachos, J Lin, E Keogh, D Gunopulos, A wavelet-based anytime algorithm for K-means clustering of time series, Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, U.S.A., May, 2003,1～3
[79] K Kalpakis, D Gada, V Puttagunta, Distance measures for effective clustering of ARIMA time-series, Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, U.S.A., 2001, 273～280
[80] K P Chan,A W Fu, Efficient time series matching by wavelets, In:Proceedings of the 15th IEEE International Conference on Data Engineering, Sydney, Australia, March,1999,126～133
[81] Y L Wu, D Agrawa1, A E Abbadi, A comparison of DFT and DWT based similarity search in time-series databases, Procedings of the Ninth International Conference on Information and Knowledge Management (CIKM 2000), Washington, U.S.A.,November, 2000, 488～495
[82] E Keogh,S Chu,M Pazzani,Ensemble-index: A new spproach to index large databases,In:SIGKDD 01, San Francisco, U.S.A.,2001,117～125
[83] K Kawagoe,T Ueda, A similarity search method of time series data with combination of Fourier and Wavelet transforms, temporal representation and reasoning, Proceedings Ninth International Symposium, New York, U.S.A., 2002, 86～92
[84] P Korn,N Sidiropoulos,C Faloutsos,et a1.,Fast nearest-neighbor search in medical image databases,In:Procedings of 22th International Conference on Very Large Data Bases,Bombay,India,1996,215～226
[85] F Korn, H Jagadish, C Faloutsos, Efficiently supporting ad hoc queries in large datasets of time sequences, Proceeding ACM SIGMOD Int’l Conference Management of Data, U.S.A., May, 1997, 289～300
[86] Liu R,Tan T, A SVD_based watermarking scheme for protecting rightful ownership,IEEETransactionson Multimedia, 2002,4(1):121～128
[87] Andrews H,Patterson C, Singular value decompositions and digital image processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1976, 24(1):26～53
[88] Tian Y,Tan T,W ang Y H,et a1., Do singular values containadequate information for face recognition, Pattern Recognition, 2003, 36(6): 649～655
[89] Valentin D, Abdi A J, Connectionist model of face processing:A survey, Pattern Recognition, 1994, 27(9): 1209～1230
[90] Keogh E, Chakrabarti K, Pazzani M and Mehrotra S, Dimensionality reduction for fast similarity search in large time series databases, Journal of Knowledge and Information Systems, 2001, 3(3): 263～286
[91] Vittorio Castelli, Alexander Thomasian, Chung-Sheng Li, CSVD: Clustering and singular value decomposition for approximate similarity search in high-dimensional spaces, IEEE Transactions on Knowledge and Data Engineering, May/June, 2003,15(3): 671～685
[92] E Keogh,M Paszani, An enhanced representation of time series which allows fast and accurate classification, clustering and releance feedback, Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, New York, U.S.A., Aug, 1998, 239～241
[93] E Keogh, P Smyth, A probabilistic approach to fast pattern matching in time series databases, Proceedings of the 3rd International Conference of Knowledge Discovery and Data Mining, Menlo Park, U.S.A., 1997, 24～20
[94] E Keogh,M Pazzani, An indexing scheme for fast similarity search in large time series databases, In:Scientific and Statistical Database Management Eleventh International Conference, Ohio, U.S.A., July 28-30, 1999,56～67
[95] Keogh, E, Chu, S, Hart D, Pazzani, An online algorithm for segmenting time series data mining, Proceedings IEEE International Conference 2001, Washington, U.S.A., 2001, 289～296
[96] E Keogh,K Chakrabarti,M Pazzani et a1., Dimensionality reduction for fast similarity search in large time series databases, In Proceedings of the ACM SIGMOD International Conference on Man agement of Data, Philadelphia, 2001,151～162
[97] E Keogh, M Paszani, Scaling up dynamic time warping for data mining applications, In Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, U.S.A., 2000,285～289
[98] E Keogh, K Chakrabarti, S Mehmtra et a1., Locally adaptive dimensionality reduction for indexing large time series databases, In ACM SIGM0D 2001,Santa Barbara,California, 2001, 151～162
[99] V Megalooikonomou, Q Wang, G Li, C Faloutsos, A multiresolution symbolic representation of time series, Proceedings of the 21st IEEE International Conference on Data Engineering (ICDE05), Tokyo, Japan, April,2005,668～679
[100] Yi, B-K and Faloutsos C , Fast time sequence indexing for arbitrary Lp norms, Proceedings of the VLDB, Cairo, Egypt, September,2000, 385～394
[101] Megalooikonomou V, Li G, Wang Q, A dimensionality reduction technique for efficient similarity analysis of time series databases, Proceedings of the 13th ACM CIKM International Conference on Information and Knowledge Management, Washington, U.S.A., November, 2004,160～161
[102] Deligiannakis A, Kotidis Y, Roussopoulos N,Compressing historical information in sensor networks,Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France, June, 2004,527～538
[103] C S Pemg,H Wang,S Zhang et a1., Landmarks:A new model for similarity-based pattern querying in time series databases, In:Data Enginering Proceedings l0th Intemational Conference, San Diego, U.S.A., 2000, 33～42
[104] Huang Y, Yu PS, Adaptive query processing for time-series data, In: Proceeding of the 5th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, San Diego, U.S.A.,1999, 282～286
[105] Giles CL, Lawrence S, Tsoi A, Noisy time series prediction using recurrent neural networks and grammatical inference, Machine Learning, 2001,44(1):161～184
[106] Lin J, Keogh E, Lonardi S, Chiu BY, A symbolic representation of time series, with implications for streaming algorithms, In: Proceeding of the 8th SIGMOD Workshop on DMKD, San Diego, U.S.A., 2003, 2～11
[107] Bagnall AJ, Janacek GJ,Clustering time series from ARMA models with clipped data, Proceeding of the 10th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Seattle, U.S.A.,2004, 49～58
[108] Fu-Lai Chung Tak-Chung Fu, Vincent Ng, Robert W P Luk, An evolutionary approach to pattern-based time series segmentation, IEEE Transactions On Evolutionary Computation, Octomber, 2004, 8(5): 471～489
[109] Li B, et al., Using fuzzy neural network clustering algorithm in the symbolization of time series, Proceedings of the 2000 IEEE Asia-Pacific Conference on Circuit and Systems, TianJin,China, 2000, 379～382
[110] Ge X, Smyth P, Deformable Markov Model Templates forTime-Series Pattern Matching, In: Proc eedings of the 6th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, Boston, U.S.A., 2000, 81～90
[111] Agrawal R, Psaila G, Wimmers E L and Zait M, Querying shapes of histories, Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland, September ,1995, 502～514
[112]王达,荣刚,时间序列的模式距离,浙江大学学报(工学版),July, 2004, 38(7): 795～798
[113] C Faloutsos, M Ranganathan, and Y Manolopoulos, Fast subsequence matching in time-series databases, Proceedings 1994 ACM-SIGMOD ConferenceManagement of DataMining, Minneapolis, May, 1994, 419～42
[114] Faloutsos C,Lin K, FastMap:A fast algorithm for indexing data-mining and visualization of traditional and multimedia datasets, Proceedings ACM SIGMOD, 1995,24(2):163～174
[115] Kassidas A, MacGregor J F, Taylor P A, Synchronization of batch trajectories using dynamic time warping, American Institute of Chemical Engineers, 1998,1(44):864～874
[116] R D Edwards and J Magee, Technical analysis of stock trends, John Magee, Spring_eld,Massachusetts, 1966, 251～256
[117] C S Perng, H Wang, S R Zhang, and D S Parker, Landmarks: A new model for similarity-based pattern querying in time series databases, Proceeding Int’l Conference Data Engineering, Los Alamitos, U.S.A., 2000, 33～42
[118] Dina Q Goldin, Paris C Kanellakis, On similarity queries for time-series data: Constraint specification and implementation, In Proceedings of the 1st Int’l Conference on the Principles and Practice of Constraint Programming, Cassis, France, September, 1995,137～153
[119] Woong-Kee Loh,Sang-Wook Kim,Kyu-Young Whang, A subsequence matching algorithm that supports normalization transform in time-series databases, Data Mining Knowledge Discovery, 2004, 9(1):5～28
[120] Aach J, Church G, Aligning gene expression time series with time warping algorithm,Bioinformatics,2001,17, 495～508
[121] Matthew D, Schmill, Tim Oates, et al., Learned models for continuous planning , In 7th International Workshop on Aritifical Intelligence and Statistics, Florida, U.S.A.,1999,278～282
[122] Caiani E G, Porta A, et al., Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume, IEEE Computers in Cardiology,1998, 25:73～76
[123] Vullings H J, et al, ECG segmentation using time-warping, In: Proceeding of 2nd Int'l Symposium on Advances in Intelligent Data Analysis, London, UK, 1997,275～285
[124] Caiani, EG Porta, A. Baselli, et al, Warped-average template technique to track on a cycle-by-cyclebasis the cardiac filling phases on left ventricular volume, IEEE Computers in Cardiology, September, 1998,25:73～76
[125] Berndt D, Cliford J, Using dynamic time warping to find paterns in time series, AAAI Workshop on Knowledge Discovery in Databases, Washington, U.S.A.,1994,229～248
[126] E Keogh, Michael J Pazzani, Scaling up dynamic time warping to massive datasets, Proceeding of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery, PKDD'99,Irvine, California, 1999,1～11
[127] Bozkaya T, Yazdani N, Ozsoyoglu Z M, Matching and indexing sequences of different lengths, Proceedings of the 6th International Conference on Information and Knowledge Management, Las Vegas, U.S.A., 1997,128～135
[128] H Shatkay,S B Zkonik, Approximate queries and representations for large data sequences, In Proceeding Int. Conference Data Engeering, Los Alamitos, U.S.A.,1996, 536～545
[129] O Y Kai,W Jia, P Zhou, X Meng, A new approach to transforming time series into symbolic sequences,In Proceeding 1st Joint BMES/EMBS Conference Serving Humanity, Advancing Technology, Atlanta, U.S.A., Octomber,1999,974
[130]方开泰,潘恩沛,聚类分析,北京:地质出版社,1982, 44～51
[131]姜宁,史忠植,文本聚类中的贝叶斯后验模型选择方法,计算机研究与发展,2002(5):580～587
[132] Park S, Chu W W, Yoon J, Won J, Similarity Search of Time-Warped Subsequences via a Suffix Tree, Information Systems, 2003, 28(7):867～883
[133] Hui Xiao, Xiao-Fei Feng, Yun-Fu Hu, A new segmented time warping distance for data mining in time series database, machine learning and cybernetics, Proceedings of 2004 International Conference, 26-29 August, 2004, 1277～1281
[134] Yingjun Weng,Zhongying Zhu,Time series clustering based on shape dynamic time warping using cloud models, Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi'an, 2-5 November ,2003(1),237～241
[135] Kadous, M W, Learning comprehensible descriptions of multivariate time series, In Proceeding of the 16th International Machine Learning, San Francisco, CA, U.S.A., 1999 , 454 - 463
[136] W.王,J.杨,P.S-L.余,在事件序列中识别局部周期模式的方法,中华人民共和国,G06F9/22,CN 1327191A,2001年12月19日。
[137] Wei W ang, Jiong Yang, Philip Shi-lung Yu, Methods for identifying partial periodic patterns and corresponding event subsequences in an event sequence, U.S.A.,G06F 9/44, US 6,718,317 B1, April 6,2004
[138] Mannila H, Toivonen H, Inkeri Verkamo, An efficient algorithms for discovery association rules, Proceedings of AAAI Workshop on Knowledge Discovery in Database, Singapore, July 1994,181～192
[139] Agrawal R, Srikant R, Fast discovery of association rules, Advances in Knowledge Discoveryand Data Mining, Cambridge:MIT Press,1996, 307～328
[140] Mannila H, Toivonen H, Verkamo A I, Discovery of frequent episodes in event sequences,Data Mining and Knowledge Discovery,1997,1(3): 259～289
[141] C Bettini, X Wang, S Jajodia,Satisfiability of quantitative temporal constraints with multiple granularities,Third International Conference on Principles and Practice of Constraint Programming(CP97), Schloss Hagenberg,Austrilia, 1997,435～445
[142] C Bettini, X Wang, S Jajodia, Mining temporal relationships with multiple granularities in time sequences, IEEE Data Engineering Bulletin,1998,21:32～38
[143] C Bettini, X Wang, S Jajodia, A general framework for time granularity and its application to temporal reasoning, Annals of Mathematics and Artificial Intelligence,1998,22:29～58
[144] Das G, Lin K, Mannila H, et al., Rule discovery from time series, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York,U.S.A.,1998,16～22
[145] Richard J Povinelli,Identifying temporal patterns for characterization and prediction of financial time series events,Proceedings of International Workshop on Temporal, Spatial and Spati-Temporal Data Mining,Lyon,France,2000,46～61
[146] Last M, Klein Y, Knowledge Discovery in time series databases, IEEE Transactions on Systems,Man,and Cybernetics, 2001, 31(1): 160～169
[147]李斌,谭立湘,解光军,非同步多时间序列中频繁模式的发现算法,软件学报,2002,13(3):410～416
[148]靳晓明,陆玉昌,石纯一,时序规则分布挖掘方法,清华大学学报,2003,43(7):972～976
[149]潘定,沈钧毅,时态数据挖掘的相似性发现技术,软件学报,2007, February, 2007, 18(2):246～258
[150] Kuo-Yu Huang and Chia-Hui Chang,SMCA: A general model for mining asynchronous periodic patterns in temporal databases,IEEE Transactions on Knowledge and Data Engineering, June, 2005, 17(6): 774～784
[151] Han, W Gong, and Y Yin, Mining segment-wise periodic patterns in time-related databases, Proceeding Fourth ACM SIGKDD Int’l Conference Knowledge Discovery and Data Mining (KDD’98), New York, U.S.A.,1998, 214～218
[152] J Han, G Dong, and Y Yin, Efficient mining of partial periodic patterns in time series database, Proceeding 15th Int’l Conference Data Engineering (ICDE’99), Australia,1999,106～115
[153] B Ozden, S Ramaswamy, A silberschatz, cyclic association rules, Proceeding 14th Int’l Conference Data Engineering (ICDE’98), Orlando, 1998, 412～421
[154] J Yang, W Wang, and P S Yu, Mining asynchronous periodic patterns in time series data, IEEE Transactions on Knowledge and Data Engineering, 2003, 15(3): 613～628
[155] Mohamed G Elfeky, Walid G Aref, Periodicity detection in time series databases, IEEE Transactions on Knowledge and Data Engineering, 2005,17(7):875～886
[156] Chang Sheng, Wynne Hsu, Mong Li Lee, Mining dense periodic patterns in time series database, Proceedings of the 22nd International Conference on Data Engineering (ICDE’06), New Orleans, Louisiana, 2006, 1～3
[157] Chang Sheng, Wynne Hsu, Mong Li Lee, Efficient mining periodic patterns in time series database, In Technical Report TR20/05, National University of Singapore, 2005,1～15
[158] W Aref, M Elfeky, A elmagarmid, incremental, online,and merge mining of partial periodic patterns in time-series databases, IEEE Transactions Knowledge and Data Engeering, March, 2004, 16(3):332～342
[159] Sheng Ma, Joseph L Hellerstein, Mining partially periodic event patterns with unknown periods, Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, April, 2001, 205～214
[160] Wenpo Yang, Guanling Lee, Efficient partial multiple periodic patterns mining without redundant rules, Proceedings of 28th Annual International Computer Software and Applications Conference, 28-30 September, 2004(1), 430～435
[161] C Berberidis, W Aref, M Atallah, et al., Multiple and partial periodicity mining in time series databases, Proceeding 15th European Conference Artificial Intelligence, Amsterdam, July 2002,370～374
[162] M Elfeky, W Aref, and A Elmagarmid, Using convolution to mine obscure periodic patterns in one pass, Proceeding Ninth Int’l Conference Extending Data Base Technology, Heraklion - Crete, Greece, March, 2004,605～620
[163] Jiong Yang, Wei Wang, and Philip Yu, InfoMiner +:Efficient mining partial periodic patterns with gap penalties,proceeding second Int’l conference data mining, Portland,December, 2002,725～728
[164] Jiong Yang, Wei Wang, Philip SYu, InfoMiner: mining surprising periodic patterns, Data Mining and Knowledge Discovery, 2004(9): 189～216
[165] Mohamed G Elfeky,Walid G Aref,Ahmed K Elmagarmid, WARP: Time warping for periodicity detection, Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005),Texas, U.S.A., November ,2005, 138～145
[166] Michail Vlachos, Philip S, Yu, et al., Structural periodic measures for time-series data, Data Mining and Knowledge Discovery, 2006, 12(1): 1～28
[167] Faloutsos C, Ranganathan M, Manolopoulos Y, Fast subsequence matching in time-series databases, SIGMOD Proceedings of Annual Conference, Minneapolis,1994,419～429
[168] Smyth P, Hidden markov models for fault detection in dynamic systems, Pattern Recognition,1994,27(1):149～164
[169] Yi B, Jagadish H, Faloutsos C, Efficient retrieval of similar time sequences under time warping, Proceeding of the 4th Int’l Conference on Data Engineering,(ICDE’98), Orlando,U.S.A.,1998,201～208
[2]曲文龙,复杂时间序列知识发现模型与算法研究,[博士学位论文],北京;北京科技大学,2006
[3] J Han and M Kamber, Data Mining: Concepts& Techniques, San Fransisco, Morgan Kaufmann Publishers, 2001, 30～33
[4]安鸿志,陈兆国,杜金观等,时间序列的分析与应用,北京:科学出版社,1983, 8
[5]肖辉,时间序列的相似性查询与异常检测,[博士学位论文],上海:复旦大学,2005
[6] Tufte E, The visual display of quantitative information, Cheshire, Connecticut: Graphics Press,1983,92～95
[7] Funkhouser H G,historical development of the graphical representation of statistical data, The American Mathematical Monthly, 1938, 45(8): 541～542
[8] Chen M S, Han J, Yu P S, Data mining: An overview form a database perspective, IEEE Transactions on Knowledeg and Data Engineering, 1996,8(6):866～883
[9] Julian Kulkarn, Richark King, Business intelligence systems and data mining, ASAS Institute White Paper,1996,68～72
[10] Fayyad U, Piatetsky S G, Smyth P, Knowledge discovery and data mining towards a unifying framework, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Oregon, Portland, 1996, 82～88
[11] Piatetsky S G, Fayyad U, Smith P, From data mining to knowledge discovery in database, AI Magazine, 1996, 17: 37～54
[12] Beckman N N, Kriegel H P, Seager B, The R-tree: An efficient and robust access method for point and rectangles, Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, Atlantic, U.S.A.,1990, 322～331
[13] Berch T, Keim D, Kriegel H P, The X-tree: an index structure for high dimensional data, Proceedings of the 22nd International Conference on Very Large Databases, San Francisco,U.S.A.,1996, 28～39
[14] Ester M, Hans-Peter K, Sander J, Knowledge discovery in spatial database, Institute for Computer Science, University of Munich, Oetingenstr, 2000,35～38
[15] KoperskiK,Han J, Discovery of spatial association rules in geographic information databases, Proceedings of the 4th International Symposium on Advances in Spatial Databases, Portland Maine, 1995, 47～66
[16] Zaiane O R, Han J, Zhu H, Mining recurrent items in multimedia with progressive resolution refinement, Proceedings of International Conference on Data Engineering, California, U.S.A., 2000, 2,461～470
[17] Keogh E J, Pazzani M J, An indexing scheme for fast similarity search in large time series databases, Proceedings of 11th International Conference on Scientific and Statistical Database Management, Ohio, U.S.A.,1999, 56～67
[18] Han J, Dong G, Yin Y, Efficient mining partial periodic paterns in time series database, Proceedings of the 15th International Conference on Data Enginieering, Sydney, Australia, 1999, 106～115
[19] GhaniR,Slattery S, Yang Y, Hypertext categorization using hyperlink paterns and meta data, Proceedings of 18th International Conference on Machine Learning, San Francisco, U.S.A., 2001,178～185
[20] Joachims T, Text categorization with support vector machines: Learning with many relevant features, Proceedings of 10th European Conference on Machine Learning, Chemnitz,Germany, 1998, 137～142
[21] Sullivan T, Reading reader reaction: A proposal for inferential analysis of web server log files, Proceedings of the 3rd Human Factors and the Web Conference, Colorado, U.S.A,1997,216～223
[22] Srivastava J, Cooley R, Deshpande M, Web usage mining: Discovery and application of usage paterns from web data, SIG KDD Explorations, 2000, 1(2): 12～23
[23] Fayyad U M, Data Mining and knowledge discovery: Making sense out of data,IEEE Expert,1996,11(5):20～25
[24] Mitra S, Pal S K, Mitra P, Data mining in soft computing framework: A Survey, IEEE Transactions on Neural Networks, 2002,13(1): 3～14
[25] Chen M-S, Han J, Yu P S, Data mining: An overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6): 866～883
[26]袁增任,人工神经元网络及其应用,北京:清华大学出版社,2000,124～130
[27] J P Bigus, Data mining with neural networks, New York: Mc Graw-Hill, 1996,56～68
[28] Qualian JR, Induction of decision trees, Machine Learning, 1986, 1:81～106
[29]李敏强等,遗传算法的基本理论与应用,北京:科学出版社,2002,1～425
[30]曾黄麟,粗集理论及其应用,重庆:重庆大学出版社,1996,29～59
[31] (美)L A扎德,陈国权译,模糊集合、语言变量及模糊逻辑,北京:科学出版社,1982,1～199
[32] W Cleveland,Visualizing data summit, NJ: Hobart Press, 1993,97～125
[33] J L Devore, Probability and statistics for engineering and sciences, New York: Duxbury Press, 1995,68～72
[34] Agrawal R,Shafer J C, Parallel mining of association rules, IEEE Transactions on Knowledge and Data Engineering,1996, 8(6): 962～969
[35] Park J S, Chen M S, Yu P S, et al., Using a hash-based method with transaction trimming for mining association rules, IEEE Transactions on Knowledge and Data Engineering, 1997, 9(5): 813～825
[36] Srikant R, Agrawal R, Mining Sequential Paterns: Generalization and performance improvement, Proceedings of the International Conference on Extending Database Technology,Avignon, France, 1996,3～17
[37] Wang K, Discovering patterns from large and dynamic sequential data, Journal of Intelligent Information Systems, 1997, 9(1): 8～33
[38] Zhang T, Ramakrishnan R, Livny M, et al., BIRCH: An efficient data clustering method for very large databases, In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996,103～114
[39] Hinneburg A, Keim D A, An eficient approach to clustering in large multimedia databases with noise, Proceedings 4th Int. Conference on Knowledge Discovery in Databases (KDD'98), New York, U.S.A., September, 1998, 58～65
[40] Quinlan, J R, Induction of decision trees, Machine Learning, 1986,1(1): 81～106
[41] Breiman L, Friedman J H, Olshen R A, et al., Classification and regression trees, Wadsworth, Belmont, 1984, 6～26
[42] Arning, A, Agrawal R, Raghavan P, A linear method for deviation detection in large databases, Proceedings 2nd Int. Conference on Knowledge Discovery and Data Mining, Portland, 1996, 164～169
[43] Knorr E M, Ng R T, and Zamar R H, Robust space transformations for distance-based operations, KDD-01, San Francisco, U.S.A., 2001, 126～135
[44]王晓晔,时间序列数据分类方法的研究:【博士学位论文],天津;天津大学,2003
[45]欧阳为民,蔡庆生,数据库中的时态数据发掘研究,计算机科学,1998,25(4): 60～63
[46] Keogh E J, Pazzani M I, An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback, Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, Menlo Park,U.S.A.,1998, 239～243
[47] Box G E, Jenkins G M, Reinsel G C,时间序列分析:预测与控制,北京:中国统计出版社,1997,125～131
[48] T Bozkaya, N Yazdani, Z M Ozsoyoglu, Matching and indexing sequences of different lengths, In Proceeding of the 6th Int Conference on Information and Knowledge Management, Las Vegas, U.S.A., 1997, 128～135
[49] Y Huang and P S. Yu, Adaptivc query processing for time-series data, In Proceedings of the 5th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, San Dicgo, U.S.A., 1999, 282～286
[50] K Kalpakis, D Gada, V Puttagunta, Distance measure for effectivc clustering of ARIMA time series, in Proceedings of the 1st IEEE Int Conf on Data Mining, San Jose, U.S.A., 2001,273～280
[51] Wai-Ho Au, Keith K, C Chan, Mining fuzzy rules for time series classification , Budapest, Hungary,July, 2004, 25～29
[52] E Keogh and S Kasctty, On the Need for Time Series Data Mining Benchmarks: A Survey and Empincal Demonstration, Data Mining and Knowledge Discovery, 2003(7): 349～371
[53] Harms, S K, Deogun J, Tadesse T, Discovering sequential association rules with contraints and time lags in multiple sequences, In proceedings of the 13th Int’l Symposium on Methodologies for Intelligent Systems, Lyon,France, 2002, 432～441
[54] Jin X, Lu Y, Shi C, Distribution discovery: Local analysis of temporal rules,In proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery and Mining, Taipei,Taiwan, 2002, 469～480
[55] Li C, Yu P, Castelli V, MALM: A framework for mining sequence database at multiple abstraction levels, Proceedings of the 7th International Conference on Information and Knowledge Management, Washington , U.S.A., November 3-7,1998,267～272
[56] Radhakrishnan N, Wilson J, Loizou D, An alternate partitioning technique to quantify the regularity of complex time series, International Journal of Bifurcation and Chaos, 2000, 10(7):1773～1779
[57] Cotofrei, P, Statistical temporal rules, In proceedings of the 15th Conference on Computational Statistics - Short Communications and Posters, Berlin, Germany, August, 2002,24～28
[58] Cotofrei P, Stoffel K, Classification rules+time= temporal rules, In proceedings of the 2002 Int’l Conference on Computational Science, Amsterdan, Netherlands, April, 2002,572～581
[59] Schittenkopf C, Tino P, Dorffner G, The benefit of information reduction for trading strategies, Report Series for Adaptive Information Systems and Management in Economics and Management Science, July, 2000, Report #45
[60] Tino P, Schittenkopf C, Dorffner G, Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams: Lessons learned from financial volatility trading, Report Series for Adaptive Information Systems and Management in Economics and Management Science, July, 2000, Report #46
[61] Yairi, T, Kato, Y, Hori, K,Fault detection by mining association rules in house-keeping data, In Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Montreal, Canada, June, 2001, 18～21
[62] Keogh E,Lin J,Trupple W, Clustering of time series subsequences is meaningless:Implications for past and future research, Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, Australia, 2003,115～122
[63] J Takeuchi, K Yamanishi, A unifying framework for detecting outliers and change points from time series, IEEE Transanctions on Knowledge and Data Engineering, 2006, 18(4):482～492
[64] Jagadish H V, Koudas Nick and Muthukrishnan S, Mining deviants in a time series database, Proceedings of 25th International Conference on Very UK Data Bases(VLDB'99), Scotland,UK,September,1999, 7～10
[65] Kotsakis E, and Wolski A, MAPS: A method for identifying and predicting aberrant behaviour in time series, Proceedings 14th Internet Conferenceerenceon Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-2001), Budapest, Hungary, June, 2001, 4～7
[66] Keogh E, Lonardi, S and Chiu W, Finding surprising paterns in a time series database in linear time and space,In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, U.S.A., July, 2002,550～556
[67] Shahabi C,Tian X and Zhao W, Tsa-tree: A wavelet_based approach to improve the efficiency of multi-level surprise and trend queries,In Proceedings 12th International Conference on Scientific and Statistical Database Management, Berlin, Germany, 2000,55～68
[68] R Agrawal, T Imielinski, A Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD Conference on Management of Data (SIGMOD'93),Washington, 1993,207～216
[69] Agrawal R, Srikant, Mining sequential patterns, In Proceeding 95 Int'l Conference Data Engineering, Taibei, Taiwan, March, 5,1995, 3～14
[70] Loether H J, McTavish D G, Descriptive and inferential statistics: An introduction, Boston: Allyn and Bacon, 1993,85～96
[71] R Agrawal, C Faloutsos, and A Swami, Eficient similarity search in sequence databases, In Int. Conference on Foundations of Data Organization (FODO),1993:69～84
[72] R Agrawal, K I Lin,H S Sawhney,et a1., Fast similarity search in the presence of noise, scaling, and translation in time-series databases, Proceeding 21st Int'l Conference Very Large Data Bases(VLDB'95), 1995, 490～501
[73] X Ge and P Smyth, Deformable markov model templates for time series pattern matching, In Proceeding ACM SIGKDD Int. Conference Knowledge Discovery Data Mining, 2000, 81～90
[74]刘世元,江浩,面向相似搜索的时间序列表示方法评述,计算机工程与应用,2004, 27:53～59
[75] F K Chan, A W Fu, C Yu, Haar wavelets for efficient similarity search of time-series: With and without time warping,Knowledge and Data Engineering, IEEE Transactions on, 2003,15 (3): 686～705
[76] T Warren Liao,Clustering of time series data- a survey,2005 Pattern Recognition Society, Pattern Recognition,2005,38 (11):1857～1874
[77] S Policker, A B Geva, Nonstationary time series analysis by temporal clustering, IEEE Transactions on Systems Man Cybernetics, 2000, 30(2): 339～343
[78] M Vlachos, J Lin, E Keogh, D Gunopulos, A wavelet-based anytime algorithm for K-means clustering of time series, Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, U.S.A., May, 2003,1～3
[79] K Kalpakis, D Gada, V Puttagunta, Distance measures for effective clustering of ARIMA time-series, Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, U.S.A., 2001, 273～280
[80] K P Chan,A W Fu, Efficient time series matching by wavelets, In:Proceedings of the 15th IEEE International Conference on Data Engineering, Sydney, Australia, March,1999,126～133
[81] Y L Wu, D Agrawa1, A E Abbadi, A comparison of DFT and DWT based similarity search in time-series databases, Procedings of the Ninth International Conference on Information and Knowledge Management (CIKM 2000), Washington, U.S.A.,November, 2000, 488～495
[82] E Keogh,S Chu,M Pazzani,Ensemble-index: A new spproach to index large databases,In:SIGKDD 01, San Francisco, U.S.A.,2001,117～125
[83] K Kawagoe,T Ueda, A similarity search method of time series data with combination of Fourier and Wavelet transforms, temporal representation and reasoning, Proceedings Ninth International Symposium, New York, U.S.A., 2002, 86～92
[84] P Korn,N Sidiropoulos,C Faloutsos,et a1.,Fast nearest-neighbor search in medical image databases,In:Procedings of 22th International Conference on Very Large Data Bases,Bombay,India,1996,215～226
[85] F Korn, H Jagadish, C Faloutsos, Efficiently supporting ad hoc queries in large datasets of time sequences, Proceeding ACM SIGMOD Int’l Conference Management of Data, U.S.A., May, 1997, 289～300
[86] Liu R,Tan T, A SVD_based watermarking scheme for protecting rightful ownership,IEEETransactionson Multimedia, 2002,4(1):121～128
[87] Andrews H,Patterson C, Singular value decompositions and digital image processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, 1976, 24(1):26～53
[88] Tian Y,Tan T,W ang Y H,et a1., Do singular values containadequate information for face recognition, Pattern Recognition, 2003, 36(6): 649～655
[89] Valentin D, Abdi A J, Connectionist model of face processing:A survey, Pattern Recognition, 1994, 27(9): 1209～1230
[90] Keogh E, Chakrabarti K, Pazzani M and Mehrotra S, Dimensionality reduction for fast similarity search in large time series databases, Journal of Knowledge and Information Systems, 2001, 3(3): 263～286
[91] Vittorio Castelli, Alexander Thomasian, Chung-Sheng Li, CSVD: Clustering and singular value decomposition for approximate similarity search in high-dimensional spaces, IEEE Transactions on Knowledge and Data Engineering, May/June, 2003,15(3): 671～685
[92] E Keogh,M Paszani, An enhanced representation of time series which allows fast and accurate classification, clustering and releance feedback, Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, New York, U.S.A., Aug, 1998, 239～241
[93] E Keogh, P Smyth, A probabilistic approach to fast pattern matching in time series databases, Proceedings of the 3rd International Conference of Knowledge Discovery and Data Mining, Menlo Park, U.S.A., 1997, 24～20
[94] E Keogh,M Pazzani, An indexing scheme for fast similarity search in large time series databases, In:Scientific and Statistical Database Management Eleventh International Conference, Ohio, U.S.A., July 28-30, 1999,56～67
[95] Keogh, E, Chu, S, Hart D, Pazzani, An online algorithm for segmenting time series data mining, Proceedings IEEE International Conference 2001, Washington, U.S.A., 2001, 289～296
[96] E Keogh,K Chakrabarti,M Pazzani et a1., Dimensionality reduction for fast similarity search in large time series databases, In Proceedings of the ACM SIGMOD International Conference on Man agement of Data, Philadelphia, 2001,151～162
[97] E Keogh, M Paszani, Scaling up dynamic time warping for data mining applications, In Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, U.S.A., 2000,285～289
[98] E Keogh, K Chakrabarti, S Mehmtra et a1., Locally adaptive dimensionality reduction for indexing large time series databases, In ACM SIGM0D 2001,Santa Barbara,California, 2001, 151～162
[99] V Megalooikonomou, Q Wang, G Li, C Faloutsos, A multiresolution symbolic representation of time series, Proceedings of the 21st IEEE International Conference on Data Engineering (ICDE05), Tokyo, Japan, April,2005,668～679
[100] Yi, B-K and Faloutsos C , Fast time sequence indexing for arbitrary Lp norms, Proceedings of the VLDB, Cairo, Egypt, September,2000, 385～394
[101] Megalooikonomou V, Li G, Wang Q, A dimensionality reduction technique for efficient similarity analysis of time series databases, Proceedings of the 13th ACM CIKM International Conference on Information and Knowledge Management, Washington, U.S.A., November, 2004,160～161
[102] Deligiannakis A, Kotidis Y, Roussopoulos N,Compressing historical information in sensor networks,Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France, June, 2004,527～538
[103] C S Pemg,H Wang,S Zhang et a1., Landmarks:A new model for similarity-based pattern querying in time series databases, In:Data Enginering Proceedings l0th Intemational Conference, San Diego, U.S.A., 2000, 33～42
[104] Huang Y, Yu PS, Adaptive query processing for time-series data, In: Proceeding of the 5th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, San Diego, U.S.A.,1999, 282～286
[105] Giles CL, Lawrence S, Tsoi A, Noisy time series prediction using recurrent neural networks and grammatical inference, Machine Learning, 2001,44(1):161～184
[106] Lin J, Keogh E, Lonardi S, Chiu BY, A symbolic representation of time series, with implications for streaming algorithms, In: Proceeding of the 8th SIGMOD Workshop on DMKD, San Diego, U.S.A., 2003, 2～11
[107] Bagnall AJ, Janacek GJ,Clustering time series from ARMA models with clipped data, Proceeding of the 10th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Seattle, U.S.A.,2004, 49～58
[108] Fu-Lai Chung Tak-Chung Fu, Vincent Ng, Robert W P Luk, An evolutionary approach to pattern-based time series segmentation, IEEE Transactions On Evolutionary Computation, Octomber, 2004, 8(5): 471～489
[109] Li B, et al., Using fuzzy neural network clustering algorithm in the symbolization of time series, Proceedings of the 2000 IEEE Asia-Pacific Conference on Circuit and Systems, TianJin,China, 2000, 379～382
[110] Ge X, Smyth P, Deformable Markov Model Templates forTime-Series Pattern Matching, In: Proc eedings of the 6th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, Boston, U.S.A., 2000, 81～90
[111] Agrawal R, Psaila G, Wimmers E L and Zait M, Querying shapes of histories, Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland, September ,1995, 502～514
[112]王达,荣刚,时间序列的模式距离,浙江大学学报(工学版),July, 2004, 38(7): 795～798
[113] C Faloutsos, M Ranganathan, and Y Manolopoulos, Fast subsequence matching in time-series databases, Proceedings 1994 ACM-SIGMOD ConferenceManagement of DataMining, Minneapolis, May, 1994, 419～42
[114] Faloutsos C,Lin K, FastMap:A fast algorithm for indexing data-mining and visualization of traditional and multimedia datasets, Proceedings ACM SIGMOD, 1995,24(2):163～174
[115] Kassidas A, MacGregor J F, Taylor P A, Synchronization of batch trajectories using dynamic time warping, American Institute of Chemical Engineers, 1998,1(44):864～874
[116] R D Edwards and J Magee, Technical analysis of stock trends, John Magee, Spring_eld,Massachusetts, 1966, 251～256
[117] C S Perng, H Wang, S R Zhang, and D S Parker, Landmarks: A new model for similarity-based pattern querying in time series databases, Proceeding Int’l Conference Data Engineering, Los Alamitos, U.S.A., 2000, 33～42
[118] Dina Q Goldin, Paris C Kanellakis, On similarity queries for time-series data: Constraint specification and implementation, In Proceedings of the 1st Int’l Conference on the Principles and Practice of Constraint Programming, Cassis, France, September, 1995,137～153
[119] Woong-Kee Loh,Sang-Wook Kim,Kyu-Young Whang, A subsequence matching algorithm that supports normalization transform in time-series databases, Data Mining Knowledge Discovery, 2004, 9(1):5～28
[120] Aach J, Church G, Aligning gene expression time series with time warping algorithm,Bioinformatics,2001,17, 495～508
[121] Matthew D, Schmill, Tim Oates, et al., Learned models for continuous planning , In 7th International Workshop on Aritifical Intelligence and Statistics, Florida, U.S.A.,1999,278～282
[122] Caiani E G, Porta A, et al., Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume, IEEE Computers in Cardiology,1998, 25:73～76
[123] Vullings H J, et al, ECG segmentation using time-warping, In: Proceeding of 2nd Int'l Symposium on Advances in Intelligent Data Analysis, London, UK, 1997,275～285
[124] Caiani, EG Porta, A. Baselli, et al, Warped-average template technique to track on a cycle-by-cyclebasis the cardiac filling phases on left ventricular volume, IEEE Computers in Cardiology, September, 1998,25:73～76
[125] Berndt D, Cliford J, Using dynamic time warping to find paterns in time series, AAAI Workshop on Knowledge Discovery in Databases, Washington, U.S.A.,1994,229～248
[126] E Keogh, Michael J Pazzani, Scaling up dynamic time warping to massive datasets, Proceeding of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery, PKDD'99,Irvine, California, 1999,1～11
[127] Bozkaya T, Yazdani N, Ozsoyoglu Z M, Matching and indexing sequences of different lengths, Proceedings of the 6th International Conference on Information and Knowledge Management, Las Vegas, U.S.A., 1997,128～135
[128] H Shatkay,S B Zkonik, Approximate queries and representations for large data sequences, In Proceeding Int. Conference Data Engeering, Los Alamitos, U.S.A.,1996, 536～545
[129] O Y Kai,W Jia, P Zhou, X Meng, A new approach to transforming time series into symbolic sequences,In Proceeding 1st Joint BMES/EMBS Conference Serving Humanity, Advancing Technology, Atlanta, U.S.A., Octomber,1999,974
[130]方开泰,潘恩沛,聚类分析,北京:地质出版社,1982, 44～51
[131]姜宁,史忠植,文本聚类中的贝叶斯后验模型选择方法,计算机研究与发展,2002(5):580～587
[132] Park S, Chu W W, Yoon J, Won J, Similarity Search of Time-Warped Subsequences via a Suffix Tree, Information Systems, 2003, 28(7):867～883
[133] Hui Xiao, Xiao-Fei Feng, Yun-Fu Hu, A new segmented time warping distance for data mining in time series database, machine learning and cybernetics, Proceedings of 2004 International Conference, 26-29 August, 2004, 1277～1281
[134] Yingjun Weng,Zhongying Zhu,Time series clustering based on shape dynamic time warping using cloud models, Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi'an, 2-5 November ,2003(1),237～241
[135] Kadous, M W, Learning comprehensible descriptions of multivariate time series, In Proceeding of the 16th International Machine Learning, San Francisco, CA, U.S.A., 1999 , 454 - 463
[136] W.王,J.杨,P.S-L.余,在事件序列中识别局部周期模式的方法,中华人民共和国,G06F9/22,CN 1327191A,2001年12月19日。
[137] Wei W ang, Jiong Yang, Philip Shi-lung Yu, Methods for identifying partial periodic patterns and corresponding event subsequences in an event sequence, U.S.A.,G06F 9/44, US 6,718,317 B1, April 6,2004
[138] Mannila H, Toivonen H, Inkeri Verkamo, An efficient algorithms for discovery association rules, Proceedings of AAAI Workshop on Knowledge Discovery in Database, Singapore, July 1994,181～192
[139] Agrawal R, Srikant R, Fast discovery of association rules, Advances in Knowledge Discoveryand Data Mining, Cambridge:MIT Press,1996, 307～328
[140] Mannila H, Toivonen H, Verkamo A I, Discovery of frequent episodes in event sequences,Data Mining and Knowledge Discovery,1997,1(3): 259～289
[141] C Bettini, X Wang, S Jajodia,Satisfiability of quantitative temporal constraints with multiple granularities,Third International Conference on Principles and Practice of Constraint Programming(CP97), Schloss Hagenberg,Austrilia, 1997,435～445
[142] C Bettini, X Wang, S Jajodia, Mining temporal relationships with multiple granularities in time sequences, IEEE Data Engineering Bulletin,1998,21:32～38
[143] C Bettini, X Wang, S Jajodia, A general framework for time granularity and its application to temporal reasoning, Annals of Mathematics and Artificial Intelligence,1998,22:29～58
[144] Das G, Lin K, Mannila H, et al., Rule discovery from time series, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York,U.S.A.,1998,16～22
[145] Richard J Povinelli,Identifying temporal patterns for characterization and prediction of financial time series events,Proceedings of International Workshop on Temporal, Spatial and Spati-Temporal Data Mining,Lyon,France,2000,46～61
[146] Last M, Klein Y, Knowledge Discovery in time series databases, IEEE Transactions on Systems,Man,and Cybernetics, 2001, 31(1): 160～169
[147]李斌,谭立湘,解光军,非同步多时间序列中频繁模式的发现算法,软件学报,2002,13(3):410～416
[148]靳晓明,陆玉昌,石纯一,时序规则分布挖掘方法,清华大学学报,2003,43(7):972～976
[149]潘定,沈钧毅,时态数据挖掘的相似性发现技术,软件学报,2007, February, 2007, 18(2):246～258
[150] Kuo-Yu Huang and Chia-Hui Chang,SMCA: A general model for mining asynchronous periodic patterns in temporal databases,IEEE Transactions on Knowledge and Data Engineering, June, 2005, 17(6): 774～784
[151] Han, W Gong, and Y Yin, Mining segment-wise periodic patterns in time-related databases, Proceeding Fourth ACM SIGKDD Int’l Conference Knowledge Discovery and Data Mining (KDD’98), New York, U.S.A.,1998, 214～218
[152] J Han, G Dong, and Y Yin, Efficient mining of partial periodic patterns in time series database, Proceeding 15th Int’l Conference Data Engineering (ICDE’99), Australia,1999,106～115
[153] B Ozden, S Ramaswamy, A silberschatz, cyclic association rules, Proceeding 14th Int’l Conference Data Engineering (ICDE’98), Orlando, 1998, 412～421
[154] J Yang, W Wang, and P S Yu, Mining asynchronous periodic patterns in time series data, IEEE Transactions on Knowledge and Data Engineering, 2003, 15(3): 613～628
[155] Mohamed G Elfeky, Walid G Aref, Periodicity detection in time series databases, IEEE Transactions on Knowledge and Data Engineering, 2005,17(7):875～886
[156] Chang Sheng, Wynne Hsu, Mong Li Lee, Mining dense periodic patterns in time series database, Proceedings of the 22nd International Conference on Data Engineering (ICDE’06), New Orleans, Louisiana, 2006, 1～3
[157] Chang Sheng, Wynne Hsu, Mong Li Lee, Efficient mining periodic patterns in time series database, In Technical Report TR20/05, National University of Singapore, 2005,1～15
[158] W Aref, M Elfeky, A elmagarmid, incremental, online,and merge mining of partial periodic patterns in time-series databases, IEEE Transactions Knowledge and Data Engeering, March, 2004, 16(3):332～342
[159] Sheng Ma, Joseph L Hellerstein, Mining partially periodic event patterns with unknown periods, Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, April, 2001, 205～214
[160] Wenpo Yang, Guanling Lee, Efficient partial multiple periodic patterns mining without redundant rules, Proceedings of 28th Annual International Computer Software and Applications Conference, 28-30 September, 2004(1), 430～435
[161] C Berberidis, W Aref, M Atallah, et al., Multiple and partial periodicity mining in time series databases, Proceeding 15th European Conference Artificial Intelligence, Amsterdam, July 2002,370～374
[162] M Elfeky, W Aref, and A Elmagarmid, Using convolution to mine obscure periodic patterns in one pass, Proceeding Ninth Int’l Conference Extending Data Base Technology, Heraklion - Crete, Greece, March, 2004,605～620
[163] Jiong Yang, Wei Wang, and Philip Yu, InfoMiner +:Efficient mining partial periodic patterns with gap penalties,proceeding second Int’l conference data mining, Portland,December, 2002,725～728
[164] Jiong Yang, Wei Wang, Philip SYu, InfoMiner: mining surprising periodic patterns, Data Mining and Knowledge Discovery, 2004(9): 189～216
[165] Mohamed G Elfeky,Walid G Aref,Ahmed K Elmagarmid, WARP: Time warping for periodicity detection, Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005),Texas, U.S.A., November ,2005, 138～145
[166] Michail Vlachos, Philip S, Yu, et al., Structural periodic measures for time-series data, Data Mining and Knowledge Discovery, 2006, 12(1): 1～28
[167] Faloutsos C, Ranganathan M, Manolopoulos Y, Fast subsequence matching in time-series databases, SIGMOD Proceedings of Annual Conference, Minneapolis,1994,419～429
[168] Smyth P, Hidden markov models for fault detection in dynamic systems, Pattern Recognition,1994,27(1):149～164
[169] Yi B, Jagadish H, Faloutsos C, Efficient retrieval of similar time sequences under time warping, Proceeding of the 4th Int’l Conference on Data Engineering,(ICDE’98), Orlando,U.S.A.,1998,201～208