A novel two-level clustering method for time series data analysis

Cheng Ping Lai, Pau Choo Chung, S. Tseng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Clustering analysis has been applied in a wild variety of fields such as biology, medicine, economics, etc. For time series clustering, dimension reduction methods like data sampling or piecewise aggregate approximation (PAA) algorithm are often applied to reduce data dimension before clustering. Consequently, the information of subsequence may be overlooked. Nevertheless, some properties of time series with the same sampling data may result in different clustering results after considering the subsequence information. In this paper, we propose a novel two-level clustering method named 2LTSC (two-level time series clustering), which considers both the whole time series, denoted as level-1 in the first level, and the subsequence information of time series, denoted as level-2 in the second level. The data length of level-2 could be different and thus is also considered in the second level in the proposed 2LTSC method. Through experimental evaluation, it is shown that the proposed two-level clustering method, which considers two different time granules at the same time, can provide different and deeper viewpoints for time series clustering analysis.

Original languageEnglish
Pages (from-to)6319-6326
Number of pages8
JournalExpert Systems with Applications
Volume37
Issue number9
DOIs
StatePublished - 1 Jan 2010

Keywords

  • Clustering
  • Data sampling
  • Dimension reduction
  • Time series analysis

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