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Conference Paper: Hierarchical Information-Theoretic Co-Clustering for high dimensional data

TitleHierarchical Information-Theoretic Co-Clustering for high dimensional data
Authors
KeywordsCo-clustering
Hierarchical clustering
Text clustering
Issue Date2011
Citation
International Journal of Innovative Computing, Information and Control, 2011, v. 7, n. 1, p. 487-500 How to Cite?
AbstractHierarchical clustering is an important technique for hierarchical data exploration applications. However, most existing hierarchial methods are based on traditional one-side clustering, which is not effective for handling high dimensional data. In this paper, we develop a partitional hierarchical co-clustering framework and propose a Hierarchical Information-Theoretical Co-Clustering (HITCC) algorithm. The algorithm conducts a series of binary partitions of objects on a data set via the Information- Theoretical Co-Clustering (ITCC) procedure, and generates a hierarchical management of object clusters. Due to simultaneously clustering of features and objects in the process of building a cluster tree, the HITCC algorithm can identify subspace clusters at different-level abstractions and acquire good clustering hierarchies. Compared with the flat ITCC algorithm and six state-of-the-art hierarchical clustering algorithms on various data sets, the new algorithm demonstrated much better performance. ICIC International © 2011 ISSN.
Persistent Identifierhttp://hdl.handle.net/10722/276884
ISSN
2010 Impact Factor: 1.667
2015 SCImago Journal Rankings: 1.722

 

DC FieldValueLanguage
dc.contributor.authorWang, Yuanyuan-
dc.contributor.authorYe, Yunming-
dc.contributor.authorLi, Xutao-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorHuang, Joshua-
dc.date.accessioned2019-09-18T08:34:56Z-
dc.date.available2019-09-18T08:34:56Z-
dc.date.issued2011-
dc.identifier.citationInternational Journal of Innovative Computing, Information and Control, 2011, v. 7, n. 1, p. 487-500-
dc.identifier.issn1349-4198-
dc.identifier.urihttp://hdl.handle.net/10722/276884-
dc.description.abstractHierarchical clustering is an important technique for hierarchical data exploration applications. However, most existing hierarchial methods are based on traditional one-side clustering, which is not effective for handling high dimensional data. In this paper, we develop a partitional hierarchical co-clustering framework and propose a Hierarchical Information-Theoretical Co-Clustering (HITCC) algorithm. The algorithm conducts a series of binary partitions of objects on a data set via the Information- Theoretical Co-Clustering (ITCC) procedure, and generates a hierarchical management of object clusters. Due to simultaneously clustering of features and objects in the process of building a cluster tree, the HITCC algorithm can identify subspace clusters at different-level abstractions and acquire good clustering hierarchies. Compared with the flat ITCC algorithm and six state-of-the-art hierarchical clustering algorithms on various data sets, the new algorithm demonstrated much better performance. ICIC International © 2011 ISSN.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Innovative Computing, Information and Control-
dc.subjectCo-clustering-
dc.subjectHierarchical clustering-
dc.subjectText clustering-
dc.titleHierarchical Information-Theoretic Co-Clustering for high dimensional data-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-78650921557-
dc.identifier.volume7-
dc.identifier.issue1-
dc.identifier.spage487-
dc.identifier.epage500-

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