Making Subsequence Time Series Clustering Meaningful
Date
2005
Authors
Chen, Jason Robert
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Publisher
Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
Recently, the startling claim was made that sequential time series clustering is meaningless. This has important consequences for a significant amount of work in the literature, since such a claim invalidates this work's contribution. In this paper, we show that sequential time series clustering is not meaningless, and that the problem highlighted in these works stem from their use of the Euclidean distance metric as the distance measure in the subsequence vector space. As a solution, we consider quite a general class of time series, and propose a regime based on two types of similarity that can exist between subsequence vectors, which give rise naturally to an alternative distance measure to Euclidean distance in the subsequence vector space. We show that, using this alternative distance measure, sequential time series clustering can indeed be meaningful. We repeat a key experiment in the work on which the "meaningless" claim was based, and show that our method leads to a successful clustering outcome.
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Keywords
Keywords: Clustering algorithms; Data mining; Euclidean distance metric; Vector space; Time series analysis
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Source
Proceedings Fifth IEEE International Conference on Data Mining
Type
Conference paper
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Restricted until
2037-12-31
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