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Article: k-means clustering with outlier removal

Titlek-means clustering with outlier removal
Authors
Keywordsk-means
Data clustering
Outlier detection
Issue Date2017
Citation
Pattern Recognition Letters, 2017, v. 90, p. 8-14 How to Cite?
Abstract© 2017 Elsevier B.V. Outlier detection is an important data analysis task in its own right and removing the outliers from clusters can improve the clustering accuracy. In this paper, we extend the k-means algorithm to provide data clustering and outlier detection simultaneously by introducing an additional “cluster” to the k-means algorithm to hold all outliers. We design an iterative procedure to optimize the objective function of the proposed algorithm and establish the convergence of the iterative procedure. Numerical experiments on both synthetic data and real data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/276542
ISSN
2017 Impact Factor: 1.954
2015 SCImago Journal Rankings: 1.225

 

DC FieldValueLanguage
dc.contributor.authorGan, Guojun-
dc.contributor.authorNg, Michael Kwok Po-
dc.date.accessioned2019-09-18T08:33:55Z-
dc.date.available2019-09-18T08:33:55Z-
dc.date.issued2017-
dc.identifier.citationPattern Recognition Letters, 2017, v. 90, p. 8-14-
dc.identifier.issn0167-8655-
dc.identifier.urihttp://hdl.handle.net/10722/276542-
dc.description.abstract© 2017 Elsevier B.V. Outlier detection is an important data analysis task in its own right and removing the outliers from clusters can improve the clustering accuracy. In this paper, we extend the k-means algorithm to provide data clustering and outlier detection simultaneously by introducing an additional “cluster” to the k-means algorithm to hold all outliers. We design an iterative procedure to optimize the objective function of the proposed algorithm and establish the convergence of the iterative procedure. Numerical experiments on both synthetic data and real data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.-
dc.languageeng-
dc.relation.ispartofPattern Recognition Letters-
dc.subjectk-means-
dc.subjectData clustering-
dc.subjectOutlier detection-
dc.titlek-means clustering with outlier removal-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patrec.2017.03.008-
dc.identifier.scopuseid_2-s2.0-85014910530-
dc.identifier.volume90-
dc.identifier.spage8-
dc.identifier.epage14-

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