计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (31): 116-118.

• 数据库、信号与信息处理 • 上一篇    下一篇

时间序列相似性半监督谱聚类

蔡世玉,夏战国,张文涛   

  1. 中国矿业大学 计算机科学与技术学院单位,江苏 徐州 221116
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2011-11-01 发布日期:2011-11-01

Semi-supervised spectral clustering of time-series similarity

CAI Shiyu,XIA Zhanguo,ZHANG Wentao   

  1. School of Computer Science & Technology,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-11-01 Published:2011-11-01

摘要: 时间序列相似度是时间序列数据挖掘的重要研究方向之一。如何利用时间序列相似度对提高时间序列数据聚类有着重要的意义。提出一种基于时间序列相似度的半监督谱聚类算法,通过选取适当的时间序列特征构造相似度与距离,在谱聚类算法的基础上利用标签数据选取初始类簇。实验表明,该算法使具有相似特征的时间序列可以很有效地被聚集到同一类中。

关键词: 时间序列, 半监督, 方差, 聚类

Abstract: Time series similarity is the important research direction of time series data mining.It is significant that how to make use of time series similarity to improve clustering of time series data.This paper presents a time series similarity-based semi-supervised spectral clustering algorithm.By selecting the appropriate features of time series to construct similarity and distance,the initial class is selected using tag data based on the spectral clustering algorithm.Results show the algorithm that makes time series with similar characteristics can be very effective to the same class are clustered.

Key words: time-series, semi-supervised, variance, clustering