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对基于聚类和遗传算法的时间序列分割算法的改进 被引量：1

Improvement of cluster-based genetic segmentation of a time series algorithm
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摘要 Vincent S.Tseng等人提出的基于聚类和遗传算法的时间序列分割算法中,对于适应值函数的定义存在缺陷,本文对此进行了改进:用归一化处理消除子序列幅度对距离计算的影响,并引入类间距使分割结果的类间差异(模式之间的差异)变得更明显。从对比算法改进前后的实验结果可以看出,这两点措施使适应值函数的精确性得到了提高,更有利于识别出子序列的模式。 The fitness value function in the algorithm proposed by Vincent Tseng S.Tseng et al based on the cluster-based genetic segmentation of time series with DWT is inadequate.Two points on the calculation of fitness value of each chromosome was proposed to improve this algorithm： data normalization was used to eliminate the influence of amplitude,and the inter-class distance was introduced to make distance between classes distinct.Experiments were conducted to compare the former and improved algorithm,and the results showed that these two improvements improved fitness value function accuracy which was more beneficial to identify sequence patterns.
作者 吴大华 何振峰
机构地区 福州大学数学与计算机科学学院
出处 《山东大学学报（理学版）》 CAS CSCD 北大核心 2010年第7期45-49,共5页 Journal of Shandong University(Natural Science)
基金 国家自然科学基金资助项目(60805042) 福州大学科技创新基金资助项目(2009-XQ-26)
关键词 时间序列 分割 聚类 遗传算法 time series segmentation clustering genetic algorithm
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献5

  • 1李爱国,覃征.在线分割时间序列数据[J].软件学报,2004,15(11):1671-1679. 被引量：27
  • 2FU T C, CHUNG F L, NG V, et al. Evolutionary segmentation of financial time series into subsequences [C ]//Proceedings of the 2001 Congress on Evolutionary Computation. Washington: IEEE Computer Society, 2001:426-430.
  • 3CHUNG F L, FU T C, NG V, et al. Evolutionary time series segmentation for stock data mining [ C]//Proceedings of the 2001 IEEE International Conference on Data Mining. Washington: IEEE Computer Society, 2002:83-90.
  • 4CHUNG F L, FU T C, NG V, et al. An evolutionary approach to pattern-based time series segmentation [ J ]. IEEE Transactions on Evolutionary Computation, 2004, 8 (5) : 471-489.
  • 5TSENG V S, CHEN Chunhao, HUANG Paichieh, et al. Cluster-based genetic segmentation of time series with DWT [ J ]. Pattern Recognition Letters, 2009, 30(13) : 1190-1197.

二级参考文献3

共引文献26

同被引文献13

  • 1肖辉,胡运发.基于分段时间弯曲距离的时间序列挖掘[J].计算机研究与发展,2005,42(1):72-78. 被引量：61
  • 2KEOGH E, CHU S, HART D, et al. An online algorithm segmenting time series [ C ]//IEEE International Conference on Data Mining. Washington: IEEE, 2001 : 289 -296.
  • 3LIN J, KEOGH E, LONARDI S, et al. A symbolic rep- resentation of time series, with implications for streaming algorithms[C]~//Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. New York: ACM, 2003: 2-11.
  • 4ALESSANDRO Camerra, THEMIS Palpanas, JIN Shieh, et al. iSAX 2. 0 : indexing and mining one billion time series [ C ]//IEEE International Conference on Data Mining. Washington: IEEE, 2010 : 1-13.
  • 5KEOGH E, RATANAMAHATANA C. Exact indexing of dynamic time warping [ C ]//Proceedings of the 28th VLDB Conference \ Knowledge and Information Sys- tems. New York: ACM, 2005: 358-380.
  • 6KEOGH E, PAZZANI M. Derivative dynamic time war- ping [ C ]//The First SIAM International Conference on Data Mining. Washington: IEEE, 2001: 1-11.
  • 7SAKOE H, CHIBA S. Dynamic programming algorithm optimization for spoken word recognition [ J ]. IEEE Transaction on Acoustics, Speech, and Signal Process, 1978, 26( 1 ) : 43-49.
  • 8MYERS C, RABINER L, ROSENBERG A. Perform- ance tradeoffs in dynamic time warping algorithms for i- solated word recognition [ J ]. IEEE Transaction on Acoustics, Speech, and Signal Process, 1980, 28 (6) : 623 -635.
  • 9WANG Q, MEGALOOIKONOMOU V. A dimensional- ity reduction technique for efficient time series similarity analysis[J]. Information Systems, 2008, 33 (1) : 115- 132.
  • 10FUCHS E, GRUBER T, NITSCHKE J, et al. Temporal data mining using shape space representation of time se- ries[J].Neurocomputing, 2010, 74(2): 379-393.

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山东大学学报（理学版）
2010年 第7期

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