A clustering method based on boosting

Frossyniotis, D; Likas, A; Stafylopatis, A

dc.contributor.author	Frossyniotis, D	en
dc.contributor.author	Likas, A	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:19:42Z
dc.date.available	2014-03-01T01:19:42Z
dc.date.issued	2004	en
dc.identifier.issn	0167-8655	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15678
dc.subject	Ensemble clustering	en
dc.subject	Partitions schemes	en
dc.subject	Unsupervised learning	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Algorithms	en
dc.subject.other	Data reduction	en
dc.subject.other	Iterative methods	en
dc.subject.other	Problem solving	en
dc.subject.other	Quality assurance	en
dc.subject.other	Boosting	en
dc.subject.other	Clustering	en
dc.subject.other	Pattern recognition	en
dc.subject.other	image classification	en
dc.title	A clustering method based on boosting	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.patrec.2003.12.018	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.patrec.2003.12.018	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g. k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality. (C) 2004 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Pattern Recognition Letters	en
dc.identifier.doi	10.1016/j.patrec.2003.12.018	en
dc.identifier.isi	ISI:000220572900004	en
dc.identifier.volume	25	en
dc.identifier.issue	6	en
dc.identifier.spage	641	en
dc.identifier.epage	654	en