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Making Subsequence Time Series Clustering Meaningful

Chen, Jason Robert


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...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Conference paper
Source: Proceedings Fifth IEEE International Conference on Data Mining
DOI: 10.1109/ICDM.2005.91


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