Independent component analysis for clustering multivariate time series data

Edmond H. C. WU, Leung Ho Philip YU

Research output: Chapter in Book/Report/Conference proceedingChapters

13 Citations (Scopus)

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. Copyright © 2005 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationAdvanced data mining and applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. proceedings
EditorsXue LI, Shuliang WANG, Zhao Yang DONG
Place of PublicationBerlin
PublisherSpringer
Pages474-482
ISBN (Electronic)9783540318774
ISBN (Print)9783540278948
DOIs
Publication statusPublished - 2005

Citation

Wu, E. H. C., & Yu, P. L. H. (2005). Independent component analysis for clustering multivariate time series data. In X. Li, S. Wang, & Z. Y. Dong (Eds.), Advanced data mining and applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005. proceedings (pp. 474-482). Berlin: Springer.

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