Interaction-Based Clustering of Multivariate Time Series

I
ICDM
2009
2009 Ninth IEEE International Conference on Data Mining
Abstract - Interaction-Based Clustering of Multivariate Time Series

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2009 Ninth IEEE International Conference on Data Mining

Miami, Florida

December 06-December 09

ISBN: 978-0-7695-3895-2

	ASCII Text	x
	Claudia Plant, Afra M. Wohlschläger, Andrew Zherdin, "Interaction-Based Clustering of Multivariate Time Series," Data Mining, IEEE International Conference on, pp. 914-919, 2009 Ninth IEEE International Conference on Data Mining, 2009.

	BibTex	x
	@article{ 10.1109/ICDM.2009.109, author = {Claudia Plant and Afra M. Wohlschläger and Andrew Zherdin}, title = {Interaction-Based Clustering of Multivariate Time Series}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2009}, issn = {1550-4786}, pages = {914-919}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.109}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Interaction-Based Clustering of Multivariate Time Series SN - 1550-4786 SP914 EP919 A1 - Claudia Plant, A1 - Afra M. Wohlschläger, A1 - Andrew Zherdin, PY - 2009 KW - Algorithms KW - Clustering methods KW - Time series VL - 0 JA - Data Mining, IEEE International Conference on ER -

Claudia Plant

Afra M. Wohlschläger

Andrew Zherdin

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.109

In this paper, we present a novel approach to clustering multivariate time series. In contrast to previous approaches, we base our cluster notion on the interactions between the univariate time series within a data object. Our objective is to assign objects with a similar intrinsic interaction pattern to a common cluster. To formalize this idea, we define a cluster by a set of mathematical models describing the cluster-specific interaction pattern. In addition, we propose interaction K-means (IKM), an efficient algorithm for partitioning clustering of multivariate time series. The cluster-specific interaction patterns detected by IKM provide valuable information for interpretation of the cluster content. An extensive experimental evaluation on synthetic and real world data demonstrates the effectiveness and efficiency of our approach.

Index Terms:

Algorithms, Clustering methods, Time series

Citation:

Claudia Plant, Afra M. Wohlschläger, Andrew Zherdin, "Interaction-Based Clustering of Multivariate Time Series," icdm, pp.914-919, 2009 Ninth IEEE International Conference on Data Mining, 2009

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