Making Subsequence Time Series Clustering Meaningful
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]
|Collections||ANU Research Publications|
|Source:||Proceedings Fifth IEEE International Conference on Data Mining|
|01_Chen_Making_Subsequence_Time_Series_2005.pdf||257.42 kB||Adobe PDF||Request a copy|
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