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Conference Paper: Independent component analysis for clustering multivariate time series data

TitleIndependent component analysis for clustering multivariate time series data
Authors
KeywordsClustering
Independent component analysis
Statistics
Time series
Issue Date2005
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Advanced Data Mining and Applications, Lecture Notes in Artificial Intelligence, Volume 3584, p. 474-482 How to Cite?
AbstractIndependent 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. © Springer-Verlag Berlin Heidelberg 2005.
Persistent Identifierhttp://hdl.handle.net/10722/110237
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorWu, EHCen_HK
dc.contributor.authorYu, PLHen_HK
dc.date.accessioned2010-09-26T01:57:07Z-
dc.date.available2010-09-26T01:57:07Z-
dc.date.issued2005en_HK
dc.identifier.citationAdvanced Data Mining and Applications, Lecture Notes in Artificial Intelligence, Volume 3584, p. 474-482en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/110237-
dc.description.abstractIndependent 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. © Springer-Verlag Berlin Heidelberg 2005.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.subjectClusteringen_HK
dc.subjectIndependent component analysisen_HK
dc.subjectStatisticsen_HK
dc.subjectTime seriesen_HK
dc.titleIndependent component analysis for clustering multivariate time series dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-26944453152en_HK
dc.identifier.hkuros133496en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-26944453152&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3584 LNAIen_HK
dc.identifier.spage474en_HK
dc.identifier.epage482en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridWu, EHC=7202128063en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK

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