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Conference Paper: On the performance of feature weighting K-means for text subspace clustering

TitleOn the performance of feature weighting K-means for text subspace clustering
Authors
KeywordsText Clustering
Feature Weighting
Convergency
Scalability
Subspace Clustering
Issue Date2005
PublisherSpringer.
Citation
6th International Conference on Web-Age Information Management (WAIM 2005), Hangzhou, China, 11-13 October 2005. In Advances in Web-Age Information Management: 6th International Conference, WAIM 2005, Hangzhou, China, October 11 – 13, 2005: Proceedings, 2005, p. 502-512 How to Cite?
AbstractText clustering is an effective way of not only organizing textual information, but discovering interesting patterns. Most existing methods, however, suffer from two main drawbacks; they cannot provide an understandable representation for text clusters, and cannot scale to very large text collections. Highly scalable text clustering algorithms are becoming increasingly relevant. In this paper, we present a performance study of a new subspace clustering algorithm for large sparse text data. This algorithm automatically calculates the feature weights in the k-means clustering process. The feature weights are used to discover clusters from subspaces of the text vector space and identify terms that represent the semantics of the clusters. A series of experiments have been conducted to test the performance of the algorithm, including resource consumption and clustering quality. The experimental results on real-world text data have shown that our algorithm quickly converges to a local optimal solution and is scalable to the number of documents, terms and the number of clusters. © Springer-Verlag Berlin Heidelberg 2005.
Persistent Identifierhttp://hdl.handle.net/10722/276792
ISBN
ISSN
2005 Impact Factor: 0.302
2020 SCImago Journal Rankings: 0.249
Series/Report no.Lecture Notes in Computer Science ; 3739

 

DC FieldValueLanguage
dc.contributor.authorJing, Liping-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorXu, Jun-
dc.contributor.authorHuang, Joshua Zhexue-
dc.date.accessioned2019-09-18T08:34:40Z-
dc.date.available2019-09-18T08:34:40Z-
dc.date.issued2005-
dc.identifier.citation6th International Conference on Web-Age Information Management (WAIM 2005), Hangzhou, China, 11-13 October 2005. In Advances in Web-Age Information Management: 6th International Conference, WAIM 2005, Hangzhou, China, October 11 – 13, 2005: Proceedings, 2005, p. 502-512-
dc.identifier.isbn9783540292272-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276792-
dc.description.abstractText clustering is an effective way of not only organizing textual information, but discovering interesting patterns. Most existing methods, however, suffer from two main drawbacks; they cannot provide an understandable representation for text clusters, and cannot scale to very large text collections. Highly scalable text clustering algorithms are becoming increasingly relevant. In this paper, we present a performance study of a new subspace clustering algorithm for large sparse text data. This algorithm automatically calculates the feature weights in the k-means clustering process. The feature weights are used to discover clusters from subspaces of the text vector space and identify terms that represent the semantics of the clusters. A series of experiments have been conducted to test the performance of the algorithm, including resource consumption and clustering quality. The experimental results on real-world text data have shown that our algorithm quickly converges to a local optimal solution and is scalable to the number of documents, terms and the number of clusters. © Springer-Verlag Berlin Heidelberg 2005.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdvances in Web-Age Information Management: 6th International Conference, WAIM 2005, Hangzhou, China, October 11 – 13, 2005: Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 3739-
dc.subjectText Clustering-
dc.subjectFeature Weighting-
dc.subjectConvergency-
dc.subjectScalability-
dc.subjectSubspace Clustering-
dc.titleOn the performance of feature weighting K-means for text subspace clustering-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/11563952_44-
dc.identifier.scopuseid_2-s2.0-33646509173-
dc.identifier.spage502-
dc.identifier.epage512-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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