Article

Independent component analysis for clustering multivariate time series data

Authors:

Edmond H. C. Wu,

Philip L. H. YuAuthors Info & Claims

ADMA'05: Proceedings of the First international conference on Advanced Data Mining and Applications

Pages 474 - 482

https://doi.org/10.1007/11527503_57

Published: 22 July 2005 Publication History

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Abstract

Independent Component Analysis (ICA) is a useful statistical method for separating mixed data sources into statistically independent patterns. In this paper, we apply ICA to transform multivariate time series data into independent components (ICs), and then propose a clustering algorithm called ICACLUS to group underlying data series according to the ICs found. This clustering algorithm can be used to identify stocks with similar stock price movement. The experiments show that this method is effective and efficient, which also outperforms other comparable clustering methods, such as K-means.

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A. Hyvarinen 1999, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks 10(3), 626-634.

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A. Hyvarinen, J. Karhunen, E. Oja, Independent Component Analysis, New York, J. Wiley, 2001.

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A. Hyvarinen and E. Oja, A fast fixed-point algorithm for independent component analysis, Neural Computation 9, 1483-1492, 1997.

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C. Jutten and J. Herault, Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing 24, 1-10, 1991.

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J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. 5th Berkeley Symp. Math. Statist, Prob., 1:281-297, 1967.

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Kremer HGünnemann SHeld ASeidl T(2012)Mining of temporal coherent subspace clusters in multivariate time series databasesProceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I10.1007/978-3-642-30217-6_37(444-455)Online publication date: 29-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30217-6_37
Cao BShen DSun JWang XYang QChen Z(2007)Detect and track latent factors with online nonnegative matrix factorizationProceedings of the 20th international joint conference on Artifical intelligence10.5555/1625275.1625708(2689-2694)Online publication date: 6-Jan-2007
https://dl.acm.org/doi/10.5555/1625275.1625708

Index Terms

Independent component analysis for clustering multivariate time series data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

Index terms have been assigned to the content through auto-classification.

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Information

Published In

ADMA'05: Proceedings of the First international conference on Advanced Data Mining and Applications

July 2005

831 pages

ISBN:354027894X

Editors:
Xue Li
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
,
Shuliang Wang
The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Queensland, China
,
Zhao Yang Dong
School of ITEE, The Univ of Queensland, St. Lucia, Wuhan, QLD, Australia

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 July 2005

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Kremer HGünnemann SHeld ASeidl T(2012)Mining of temporal coherent subspace clusters in multivariate time series databasesProceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I10.1007/978-3-642-30217-6_37(444-455)Online publication date: 29-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30217-6_37
Cao BShen DSun JWang XYang QChen Z(2007)Detect and track latent factors with online nonnegative matrix factorizationProceedings of the 20th international joint conference on Artifical intelligence10.5555/1625275.1625708(2689-2694)Online publication date: 6-Jan-2007
https://dl.acm.org/doi/10.5555/1625275.1625708

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Independent arrays or independent time courses for gene expression time series data analysis

Clustering Based on Independent Component

Independent Component Analysis for the objective classification of globular clusters of the galaxy NGC 5128

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