book chapter · IGI Global eBooks · 2011
Subsequence Time Series Clustering
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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. Request access from your librarian to read this chapter's full text.
Cite this paper
Chen, J. (2011). Subsequence Time Series Clustering. IGI Global eBooks. https://doi.org/10.4018/9781605660103.ch286
Chen, Jason. “Subsequence Time Series Clustering.” IGI Global eBooks, 2011, https://doi.org/10.4018/9781605660103.ch286.
Chen, Jason. 2011. “Subsequence Time Series Clustering.” IGI Global eBooks. https://doi.org/10.4018/9781605660103.ch286.
Chen, J. (2011) ‘Subsequence Time Series Clustering’, IGI Global eBooks. Available at: https://doi.org/10.4018/9781605660103.ch286.
@incollection{chen2011subsequence,
title = {{Subsequence Time Series Clustering}},
author = {Chen, Jason},
booktitle = {IGI Global eBooks},
year = {2011},
doi = {10.4018/9781605660103.ch286}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.