基于聚类的物联网监测点相邻关系的判定与分析
|
摘要
物联网监测点相邻关系判定是实现物联网监测异常数据审核时需要解决的一个重要问题。为了克服传统的基于行政区域或地理位置直接指定相邻关系存在的不足,采用聚类分析方法,用轮廓系数作为确定簇数和选择算法的依据,实现了一种基于历史监测数据的物联网监测点逻辑相邻关系判定方法。使用实际监测数据对该方法进行了验证,实验结果表明,所得到的相邻关系符合监测数据的实际关系,能够为物联网监测数据有效性审核提供更加科学合理的处理依据。
It is very important to determine the neighbor relationship of the monitoring points in Internet of Things when abnormal monitoring data is audited. Traditionally the neighbor relationship is directly set according to administrative area or geographical location of monitoring points, whereas some shortcomings need to be overcome. We propose a method for determining the neighbor relationship of the monitoring points in Internet of Things based on historical monitoring data. It adopts the clustering analysis method, and chooses cluster number and clustering algorithm according to silhouette coefficients. Experiments on real monitoring data show that the neighbor relationship obtained through the proposed method is more in line with the true situation, and can provide a more scientific and reasonable basis for verifying the validity of the monitoring data of Internet of Things.
It is very important to determine the neighbor relationship of the monitoring points in Internet of Things when abnormal monitoring data is audited. Traditionally the neighbor relationship is directly set according to administrative area or geographical location of monitoring points, whereas some shortcomings need to be overcome. We propose a method for determining the neighbor relationship of the monitoring points in Internet of Things based on historical monitoring data. It adopts the clustering analysis method, and chooses cluster number and clustering algorithm according to silhouette coefficients. Experiments on real monitoring data show that the neighbor relationship obtained through the proposed method is more in line with the true situation, and can provide a more scientific and reasonable basis for verifying the validity of the monitoring data of Internet of Things.
引文
[1] The Institute of Atmospheric Physics,Chinese Academy of Sciences,China National Environmental Monitoring Centre.Design of regional ambient air quality business forecasting platform[M].Beijing:China Environmental Science Press,2015.(in Chinese)
[2] Tang Ling,Fang Ruo-chen,Li Fu-ling,et al.Development of atmospheric environmental monitoring data audit and analysis system[J].Journal of North China Institute of Science and Technology,2016,13(4):96-101.(in Chinese)
[3] Zhou Zhi-hua.Machine learning [M].Beijing:Tsinghua University Press,2016.(in Chinese)
[4] Jin Jian-guo.Review of clustering method[J].Computer Science,2014,41(11A):288-293.(in Chinese)
[5] He Jin-rong,Ding Li-xin,Hu Qing-hui,et al.Properties of high-dimensional data space and metric choice[J].Computer Science,2014,41(3):212-217.(in Chinese)
[6] Tao Yang,Li Peng-liang,Shen Jing-hong,et al.Similarity search of time series based on dynamic time warping[J].Computer Engineering and Design,2017,38(12):3291-3297.(in Chinese)
[7] Wang X Y,Mueen A,Ding H,et al.Experimental comparison of representation methods and distance measures for time series data[J].Data Mining and Knowledge Discovery,2013,26(2):275-309.
[8] Chen Hai-yan,Liu Chen-hui,Sun Bo.Survey on similarity measurement of time series data mining[J].Control and Decision,2017,32(1):1-11.(in Chinese)
[9] Han Jia-wei,Kamber M,Pei Jian.Data mining:Concept and techniques[M].3rd Edition.Fan Ming,Meng Xiao-feng,Translation.Beijing:China Machine Press,2012.(in Chinese)
[10] Lantz B.Machine learing with R[M].Li Hong-cheng,Xu Jin-wei,Li Jian,Translation.Beijing:China Machine Press,2015.(in Chinese)
[1] 中国科学院大气物理研究所,中国环境监测总站。区域环境空气质量业务预报平台设计[M].北京:中国环境出版社.2015.
[2] 唐菱,方若晨,李芙玲,等.大气环境监测数据审核和分析系统的设计与实现[J].华北科技学院学报,2016,13(4):96-101.
[3] 周志华.机器学习[M].北京:清华大学出版社,2016.
[4] 金建国.聚类方法综述[J].计算机科学,2014,41(11A):288-293.
[5] 何进荣,丁立新,胡庆辉,等.高维数据空间的性质及度量选择[J].计算机科学,2014,41(3):212-217.
[6] 陶洋,李鹏亮,沈敬红,等.基于DTW的时间序列流相似性搜索方法[J].计算机工程与设计,2017,38(12):3291-3297.
[8] 陈海燕,刘晨晖,孙博.时间序列数据挖掘的相似性度量综述[J].控制与决策,2017,32(1):1-11.
[9] Han Jia-wei,Kamber M,Pei Jian.数据挖掘:概念与技术[M].第3版.范明,孟小峰,译.北京:机械工业出版社,2012.
[10] Lantz B.机器学习与R语言[M].李洪成,许金炜,李舰,译.北京:机械工业出版社,2015.
[2] Tang Ling,Fang Ruo-chen,Li Fu-ling,et al.Development of atmospheric environmental monitoring data audit and analysis system[J].Journal of North China Institute of Science and Technology,2016,13(4):96-101.(in Chinese)
[3] Zhou Zhi-hua.Machine learning [M].Beijing:Tsinghua University Press,2016.(in Chinese)
[4] Jin Jian-guo.Review of clustering method[J].Computer Science,2014,41(11A):288-293.(in Chinese)
[5] He Jin-rong,Ding Li-xin,Hu Qing-hui,et al.Properties of high-dimensional data space and metric choice[J].Computer Science,2014,41(3):212-217.(in Chinese)
[6] Tao Yang,Li Peng-liang,Shen Jing-hong,et al.Similarity search of time series based on dynamic time warping[J].Computer Engineering and Design,2017,38(12):3291-3297.(in Chinese)
[7] Wang X Y,Mueen A,Ding H,et al.Experimental comparison of representation methods and distance measures for time series data[J].Data Mining and Knowledge Discovery,2013,26(2):275-309.
[8] Chen Hai-yan,Liu Chen-hui,Sun Bo.Survey on similarity measurement of time series data mining[J].Control and Decision,2017,32(1):1-11.(in Chinese)
[9] Han Jia-wei,Kamber M,Pei Jian.Data mining:Concept and techniques[M].3rd Edition.Fan Ming,Meng Xiao-feng,Translation.Beijing:China Machine Press,2012.(in Chinese)
[10] Lantz B.Machine learing with R[M].Li Hong-cheng,Xu Jin-wei,Li Jian,Translation.Beijing:China Machine Press,2015.(in Chinese)
[1] 中国科学院大气物理研究所,中国环境监测总站。区域环境空气质量业务预报平台设计[M].北京:中国环境出版社.2015.
[2] 唐菱,方若晨,李芙玲,等.大气环境监测数据审核和分析系统的设计与实现[J].华北科技学院学报,2016,13(4):96-101.
[3] 周志华.机器学习[M].北京:清华大学出版社,2016.
[4] 金建国.聚类方法综述[J].计算机科学,2014,41(11A):288-293.
[5] 何进荣,丁立新,胡庆辉,等.高维数据空间的性质及度量选择[J].计算机科学,2014,41(3):212-217.
[6] 陶洋,李鹏亮,沈敬红,等.基于DTW的时间序列流相似性搜索方法[J].计算机工程与设计,2017,38(12):3291-3297.
[8] 陈海燕,刘晨晖,孙博.时间序列数据挖掘的相似性度量综述[J].控制与决策,2017,32(1):1-11.
[9] Han Jia-wei,Kamber M,Pei Jian.数据挖掘:概念与技术[M].第3版.范明,孟小峰,译.北京:机械工业出版社,2012.
[10] Lantz B.机器学习与R语言[M].李洪成,许金炜,李舰,译.北京:机械工业出版社,2015.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |