关联规则算法研究及其在多媒体教学评价数据分析中的应用
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摘要
随着数据库应用的不断深化,数据库的规模急剧膨胀,人们需要对这些数据进行分析,从中发现有价值的信息。数据挖掘已经成为机器学习、人工智能、数据库等领域的研究热点。它包含关联规则挖掘、预测、分类、聚类、演化分析等多种技术手段。其中关联规则挖掘是一种主要的,也是用途最广的数据挖掘方法。
本文即对数据挖掘中的关联规则进行系统研究,深入分析了关联规则的传统支持度-置信度框架、相关度和有效度等衡量标准,并在此基础上将T检验思想引入到了关联规则的衡量中,提出了一种新的关联规则衡量标准-影响度。
在对多媒体教学评价现状和相关理论分析的基础上,作者提出了多媒体教学评价的基本原则和多媒体教学评价数据分析的目的,指出了本研究课题中关联规则在多媒体教学评价数据分析中的应用方向。
本文将关联规则衡量标准-影响度应用到多媒体教学质量评价数据分析中。采用J2EE的体系结构,用Webwork+Spring+Hibernate架构设计并开发了一个多层的教学评价数据挖掘系统,有效地提高系统的可靠性、可扩展性、可重用性和可维护性。并利用该系统对多媒体教学质量评价数据进行了分析,系统运行结果表明,利用将影响度作为关联规则的衡量标准寻找多媒体教学评价数据中潜在的关联性是可行的、有价值的,可以有效的克服现有衡量标准的一些不足,减少冗余规则的产生。
With the deepening of the application of database, the size of database expands quickly, people need to analyze these data and find the worthy information. Consequently data mining has become a research area with increasing importance. It includes lots of measures such as association rules mining, classification and prediction, clustering analysis and evolvement analysis. The main technique among the data mining measures is the association rules mining, which is also the most widely used data mining measure.
In this paper, association rule mining was studied, researched and analyzed deep.And the author analyze and discuss the support-confidence framework, the correlativity and the validity. With introducing T-Testing, the Effect as a new evaluation criterion for association rules is proposed.
The author analyze the current situation of Multi-media Teaching Evaluation and the related theory. Basic principles and data analysis purposes of Multi-media Teaching Evaluation is proposed in this paper. Following the author provide applications of the association rules in the analysis of Multi-media Teaching evaluation data in this research.
In this paper,the association rules algorithm is introduced in the analysis of Multi-media Teaching evaluation data. A multi-layer data mining system based on the popular J2EE framework is presented. The system is modeled and developed separately using Webwork + Spring + Hibernate frame, which improve the maintainability, the reusability and the extendibility. The system is introduced in the analysis of multi-media teaching evaluation data. The result shows that introducing effect based on common approach to association rules mining for the analysis of Multi-media Teaching evaluation data is feasible and valuable and can not only effectively overcome the shortage of the existing evaluation criterion for association rules but also reduce the creation of redundant rules.
本文即对数据挖掘中的关联规则进行系统研究,深入分析了关联规则的传统支持度-置信度框架、相关度和有效度等衡量标准,并在此基础上将T检验思想引入到了关联规则的衡量中,提出了一种新的关联规则衡量标准-影响度。
在对多媒体教学评价现状和相关理论分析的基础上,作者提出了多媒体教学评价的基本原则和多媒体教学评价数据分析的目的,指出了本研究课题中关联规则在多媒体教学评价数据分析中的应用方向。
本文将关联规则衡量标准-影响度应用到多媒体教学质量评价数据分析中。采用J2EE的体系结构,用Webwork+Spring+Hibernate架构设计并开发了一个多层的教学评价数据挖掘系统,有效地提高系统的可靠性、可扩展性、可重用性和可维护性。并利用该系统对多媒体教学质量评价数据进行了分析,系统运行结果表明,利用将影响度作为关联规则的衡量标准寻找多媒体教学评价数据中潜在的关联性是可行的、有价值的,可以有效的克服现有衡量标准的一些不足,减少冗余规则的产生。
With the deepening of the application of database, the size of database expands quickly, people need to analyze these data and find the worthy information. Consequently data mining has become a research area with increasing importance. It includes lots of measures such as association rules mining, classification and prediction, clustering analysis and evolvement analysis. The main technique among the data mining measures is the association rules mining, which is also the most widely used data mining measure.
In this paper, association rule mining was studied, researched and analyzed deep.And the author analyze and discuss the support-confidence framework, the correlativity and the validity. With introducing T-Testing, the Effect as a new evaluation criterion for association rules is proposed.
The author analyze the current situation of Multi-media Teaching Evaluation and the related theory. Basic principles and data analysis purposes of Multi-media Teaching Evaluation is proposed in this paper. Following the author provide applications of the association rules in the analysis of Multi-media Teaching evaluation data in this research.
In this paper,the association rules algorithm is introduced in the analysis of Multi-media Teaching evaluation data. A multi-layer data mining system based on the popular J2EE framework is presented. The system is modeled and developed separately using Webwork + Spring + Hibernate frame, which improve the maintainability, the reusability and the extendibility. The system is introduced in the analysis of multi-media teaching evaluation data. The result shows that introducing effect based on common approach to association rules mining for the analysis of Multi-media Teaching evaluation data is feasible and valuable and can not only effectively overcome the shortage of the existing evaluation criterion for association rules but also reduce the creation of redundant rules.
引文
[1] R.Agrawal, Imielinski, and A. Swami, Mining association rules between sets of items in large databases,In: Proc.of the ACM SIGMOD Conference on Management of Data,Washington,D.C, 1993,5:207-216
[2] 刘宇奇,陆一平,查建中,贾凌燕.矩形块划分的二维空间数据挖掘算法及其应用. 北京交通大学学报. 2005,4(29): 107-110
[3] J.S.Park, M.S.Chen, and P.S.Yu.An effective hash-based algorithm for mining association rules, Proceedings of ACM SIGMOD International Conference on Management of Data, 1995, 175-186
[4] J.L.Lin, and M.H.Dunham, Mining association rules: Anti-skew algorithms, Proceedings of the International Conference on Data Engingeering, Orlando, 1998
[5] 邓丰义,刘震宇.基于模式矩阵的FP2growt h 改进算法. 厦门大学学报(自然科学版),2005,5(44) :629-633
[6] 刘景春. 快速关联规则挖掘算法. 佳木斯大学学报(自然科学版. 2004, 2(22): 151-156
[7] 端宵燕. 基于数据仓库的现代教育评价系统. 教育信息化. 2004, 71-72
[8] 王陆,李亚文. 基于 OLAP 技术的教学诊断与评价模型. 计算机工程. 2003, 5(29):49-50
[9] S.Brin, R.Motwani, J.D.Ullman, Dynamic Itemset counting and implication rules for market basket data, In ACM SIGMOD International Conference On the Management of Data, 1997
[10] J.S.Park, M.S.Chen, and P.S.Yu, Efficient parallel data mining of association rules, 4th International Conference on Information and Knowledge Management, 1995
[11] M.J.Zaki,S.Parthasarathy,W.Li, A localized algorithm for parallel association mining, 9th Annual ACM Symposium on Parallel Algorithms and Architectures, 1997
[12] 王杰,张静,张继生,曾子维. 数据挖掘中关联规则的研究与论证. 鞍山科技大学学报,2005, 2(28): 124-126
[13] 来升强,朱建平. 数据挖掘中关联规则算法的发展趋势.统计与信息论坛,2005, 3(20):16-20
[14] J.S.Park, M.S.Chen, and P.S.Yu, Efficient parallel data mining of association rules, 4th International Conference on Information and Knowledge Management, 1995
[15] 周欣,沙朝锋,朱扬勇等.兴趣度—关联规则的另一个阈值.计算机研究与发展.2000,7(5):627-633
[16] 朱建平,谢邦昌. 数据挖掘中关联规则的提升及其应用. 统计研究, 2004, 12, 34-39
[17] S.Brin, R.Motwani, J.D.Ullman, Dynamic Itemset counting and implication rules for market basket data, In ACM SIGMOD, International Conference On the Management of Data, 1997
[18] 邓丰义,刘震宇. 基于模式矩阵的 FP2growth 改进算法. 厦门大学学报(自然科学版),2005, 5(44):629-633
[19] 曲守宁等.关联规则算法研究及其在教学系统中的应用.计算机系统应用.2005, 4: 20-23
[20] 张彦钊,李霞.关联规则在教学评价数据分析中的应用.微计算机应用. 2005, 5(25): 17-19
[21] 陈辉,向伟忠,单健. 关联规则挖掘在教师教学评价系统中的应用. 南华大学学报. 2005, 1(19):05-07
[22] 李芳,王恒山,吕丽娟.关联规则在教学管理决策支持中的应用.上海理工大学学报,2005, 3(27): 24-27
[23] 毛国君,段立娟,王石等.数据挖掘原理与算法.清华大学出版社.2005,7 :66-67
[24] 伊卫国,卫金茂,王名扬.增量关联规则的向量法挖掘. 计算机工程与应用, 2004, 181-183
[25] R.Srikant, and R.Agrawal.Mining generalized association rules.Proceedings of the 215 Intl.Conf.on Very Large Database, 1995, 407-419
[26] R.Srikant,R.Agrawal.Mining Quantitative Association Rules in Large Relational Tables.Proceedings of ACM-SIGMOD Intl Conference on Management of Data, Montreal, Canada, June 1996, 1-12
[27] H.Liu, H.Lu, L. Feng, F. Hussain.Efficient search of reliable exceptions. In N.Zhong andL. Zhou, editors, proceeding of the 3th Pacific-Asia Conference on knowledge discovery and data mining(PAKDD'99). Beijin Q. China, avril. 1999, 194-203
[28] A.Savascre, E.Onuccinski and S.Navathe.Mining for strong negative associations in a large database of customer transactions. In proceedings of 14' h Intl.Conf.on data engineering OCDE'98, Orlando, Florida, USA, February, 1998,494-502
[29] 罗可,郗东妹.采掘有效的关联规则. 小型微型计算机系统,2005,8(8):1374-137 5
[30] 宋旭东,翟坤,高卫东.关联规则评价指标的研究. 微计算机信息,2007,4(3):174- 175
[31] 李伟东,倪志伟,刘晓.基于兴趣度的关联规则挖掘. 计算机技术与发展,2007, 6(6):80-82
[32] 罗可,吴杰.关联规则衡量标准的研究. 控制与决策,2003(5):277-280
[33] Brin S, Motwani R,Ullman JD,et al.Dynamic itemset counting and implication rules for market basket Data.Proceedings of the ACM SIGMOD Conf on Management of Data.Tucsom,USA,1997,207-216.
[34] 张新霞,王耀青.基于统计相关性的兴趣度关联规则的挖掘.计算机工程与科 学 .2003 (3), 60-62.
[35] 李隆庚.基于层次分析法的多媒体教学效果评价研究.河北工业科技. 2007, 7(4):220-222
[36] 李焱,张峰.多媒体课堂教学效果评价研究.福建电脑,2007(5):191
[37] 罗国强.多媒体教学的评价模型.广东广播电视大学学报. 13(51):19-22
[38] 夏征农.辞海.上海:上海辞书出版社,1989
[39] 朱原译.Lonbgman dictionary of contemporary English(English-Chinese).北京:商务印书馆,1998.
[40] Australia Universitier Quality Agency(AUQA). http://www .auqa. edu.au/quality- hancement/index.html,Glossary and Abbreviations,2005
[41] Philip Crosby Associates.http://www.philipcrosby.com/index,Philip B.Crosby Biography, 2002
[42] The European Organization for Quality.http://www.eoq.org/index htm,2005.
[43] 国际标准化组织.http://www.iso.org/iso/en/iso9000-14000/articles/pdf/view point.pdf,2005
[44] 刘立户编著.全面质量管理.北京:北京大学出版社,2004
[45] 程凤春.教学全面质量管理.北京:教育科学出版社,2004.
[46] 程伯基.医学教育的质量保证.医学教育探索,2003, 2(2):1-3
[47] 唐善成.Webwork原理初探.电脑知识与技术应用研究2005.6(3),82-85
[2] 刘宇奇,陆一平,查建中,贾凌燕.矩形块划分的二维空间数据挖掘算法及其应用. 北京交通大学学报. 2005,4(29): 107-110
[3] J.S.Park, M.S.Chen, and P.S.Yu.An effective hash-based algorithm for mining association rules, Proceedings of ACM SIGMOD International Conference on Management of Data, 1995, 175-186
[4] J.L.Lin, and M.H.Dunham, Mining association rules: Anti-skew algorithms, Proceedings of the International Conference on Data Engingeering, Orlando, 1998
[5] 邓丰义,刘震宇.基于模式矩阵的FP2growt h 改进算法. 厦门大学学报(自然科学版),2005,5(44) :629-633
[6] 刘景春. 快速关联规则挖掘算法. 佳木斯大学学报(自然科学版. 2004, 2(22): 151-156
[7] 端宵燕. 基于数据仓库的现代教育评价系统. 教育信息化. 2004, 71-72
[8] 王陆,李亚文. 基于 OLAP 技术的教学诊断与评价模型. 计算机工程. 2003, 5(29):49-50
[9] S.Brin, R.Motwani, J.D.Ullman, Dynamic Itemset counting and implication rules for market basket data, In ACM SIGMOD International Conference On the Management of Data, 1997
[10] J.S.Park, M.S.Chen, and P.S.Yu, Efficient parallel data mining of association rules, 4th International Conference on Information and Knowledge Management, 1995
[11] M.J.Zaki,S.Parthasarathy,W.Li, A localized algorithm for parallel association mining, 9th Annual ACM Symposium on Parallel Algorithms and Architectures, 1997
[12] 王杰,张静,张继生,曾子维. 数据挖掘中关联规则的研究与论证. 鞍山科技大学学报,2005, 2(28): 124-126
[13] 来升强,朱建平. 数据挖掘中关联规则算法的发展趋势.统计与信息论坛,2005, 3(20):16-20
[14] J.S.Park, M.S.Chen, and P.S.Yu, Efficient parallel data mining of association rules, 4th International Conference on Information and Knowledge Management, 1995
[15] 周欣,沙朝锋,朱扬勇等.兴趣度—关联规则的另一个阈值.计算机研究与发展.2000,7(5):627-633
[16] 朱建平,谢邦昌. 数据挖掘中关联规则的提升及其应用. 统计研究, 2004, 12, 34-39
[17] S.Brin, R.Motwani, J.D.Ullman, Dynamic Itemset counting and implication rules for market basket data, In ACM SIGMOD, International Conference On the Management of Data, 1997
[18] 邓丰义,刘震宇. 基于模式矩阵的 FP2growth 改进算法. 厦门大学学报(自然科学版),2005, 5(44):629-633
[19] 曲守宁等.关联规则算法研究及其在教学系统中的应用.计算机系统应用.2005, 4: 20-23
[20] 张彦钊,李霞.关联规则在教学评价数据分析中的应用.微计算机应用. 2005, 5(25): 17-19
[21] 陈辉,向伟忠,单健. 关联规则挖掘在教师教学评价系统中的应用. 南华大学学报. 2005, 1(19):05-07
[22] 李芳,王恒山,吕丽娟.关联规则在教学管理决策支持中的应用.上海理工大学学报,2005, 3(27): 24-27
[23] 毛国君,段立娟,王石等.数据挖掘原理与算法.清华大学出版社.2005,7 :66-67
[24] 伊卫国,卫金茂,王名扬.增量关联规则的向量法挖掘. 计算机工程与应用, 2004, 181-183
[25] R.Srikant, and R.Agrawal.Mining generalized association rules.Proceedings of the 215 Intl.Conf.on Very Large Database, 1995, 407-419
[26] R.Srikant,R.Agrawal.Mining Quantitative Association Rules in Large Relational Tables.Proceedings of ACM-SIGMOD Intl Conference on Management of Data, Montreal, Canada, June 1996, 1-12
[27] H.Liu, H.Lu, L. Feng, F. Hussain.Efficient search of reliable exceptions. In N.Zhong andL. Zhou, editors, proceeding of the 3th Pacific-Asia Conference on knowledge discovery and data mining(PAKDD'99). Beijin Q. China, avril. 1999, 194-203
[28] A.Savascre, E.Onuccinski and S.Navathe.Mining for strong negative associations in a large database of customer transactions. In proceedings of 14' h Intl.Conf.on data engineering OCDE'98, Orlando, Florida, USA, February, 1998,494-502
[29] 罗可,郗东妹.采掘有效的关联规则. 小型微型计算机系统,2005,8(8):1374-137 5
[30] 宋旭东,翟坤,高卫东.关联规则评价指标的研究. 微计算机信息,2007,4(3):174- 175
[31] 李伟东,倪志伟,刘晓.基于兴趣度的关联规则挖掘. 计算机技术与发展,2007, 6(6):80-82
[32] 罗可,吴杰.关联规则衡量标准的研究. 控制与决策,2003(5):277-280
[33] Brin S, Motwani R,Ullman JD,et al.Dynamic itemset counting and implication rules for market basket Data.Proceedings of the ACM SIGMOD Conf on Management of Data.Tucsom,USA,1997,207-216.
[34] 张新霞,王耀青.基于统计相关性的兴趣度关联规则的挖掘.计算机工程与科 学 .2003 (3), 60-62.
[35] 李隆庚.基于层次分析法的多媒体教学效果评价研究.河北工业科技. 2007, 7(4):220-222
[36] 李焱,张峰.多媒体课堂教学效果评价研究.福建电脑,2007(5):191
[37] 罗国强.多媒体教学的评价模型.广东广播电视大学学报. 13(51):19-22
[38] 夏征农.辞海.上海:上海辞书出版社,1989
[39] 朱原译.Lonbgman dictionary of contemporary English(English-Chinese).北京:商务印书馆,1998.
[40] Australia Universitier Quality Agency(AUQA). http://www .auqa. edu.au/quality- hancement/index.html,Glossary and Abbreviations,2005
[41] Philip Crosby Associates.http://www.philipcrosby.com/index,Philip B.Crosby Biography, 2002
[42] The European Organization for Quality.http://www.eoq.org/index htm,2005.
[43] 国际标准化组织.http://www.iso.org/iso/en/iso9000-14000/articles/pdf/view point.pdf,2005
[44] 刘立户编著.全面质量管理.北京:北京大学出版社,2004
[45] 程凤春.教学全面质量管理.北京:教育科学出版社,2004.
[46] 程伯基.医学教育的质量保证.医学教育探索,2003, 2(2):1-3
[47] 唐善成.Webwork原理初探.电脑知识与技术应用研究2005.6(3),82-85
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