Subsequence Time Series Clustering

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Author(s): Jason Chen (Australian National University, Australia)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch286

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Abstract

Clustering analysis is a tool used widely in the Data Mining community and beyond (Everitt et al. 2001). In essence, the method allows us to “summarise” the information in a large data set X by creating a very much smaller set C of representative points (called centroids) and a membership map relating each point in X to its representative in C. An obvious but special type of data set that one might want to cluster is a time series data set. Such data has a temporal ordering on its elements, in contrast to non-time series data sets. In this article we explore the area of time series clustering, focusing mainly on a surprising recent result showing that the traditional method for time series clustering is meaningless. We then survey the literature of recent papers and go on to argue how time series clustering can be made meaningful.

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