CUZ: An improved clustering algorithm

Aslanidis, T; Souliou, D; Polykrati, K

dc.contributor.author	Aslanidis, T	en
dc.contributor.author	Souliou, D	en
dc.contributor.author	Polykrati, K	en
dc.date.accessioned	2014-03-01T02:45:13Z
dc.date.available	2014-03-01T02:45:13Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32207
dc.subject	Clustering	en
dc.subject	Data mining	en
dc.subject	Hierarchical	en
dc.subject	Large databases	en
dc.subject	Sorting	en
dc.subject.other	Chlorine compounds	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Curing	en
dc.subject.other	Decision support systems	en
dc.subject.other	Drying	en
dc.subject.other	Flow of solids	en
dc.subject.other	Information management	en
dc.subject.other	Information technology	en
dc.subject.other	Search engines	en
dc.subject.other	Technology	en
dc.subject.other	Clustering	en
dc.subject.other	Convex shapes	en
dc.subject.other	Data mining	en
dc.subject.other	Hierarchical	en
dc.subject.other	International conferences	en
dc.subject.other	Large databases	en
dc.subject.other	New algorithm	en
dc.subject.other	Non-convex shapes	en
dc.subject.other	Sorting	en
dc.subject.other	Clustering algorithms	en
dc.title	CUZ: An improved clustering algorithm	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CIT.2008.Workshops.118	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CIT.2008.Workshops.118	en
heal.identifier.secondary	4568477	en
heal.publicationDate	2008	en
heal.abstract	Clustering is for many years now one of the most complex and most studied problems in data mining. Until now the most commonly used algorithm for finding groups of similar objects in large databases is CURE [I]. The main advantage of CURE, compared to other clustering algorithms, is its ability to identify non spherical or rectangular shaped objects. In this paper we present a new algorithm called CUZ (Clustering Using Zones). The main innovation of CUZ lies in the technique that it uses to calculate the representatives. This technique overcomes the problem of identifying clusters with non-convex shapes. Experimental results show that CUZ is a generally competitive technique, while it is particularly adequate when we have to do with clusters that do not have convex shapes. © 2008 IEEE. DOI 10.1109/CIT.2008.Workshops.118.	en
heal.journalName	Proceedings - 8th IEEE International Conference on Computer and Information Technology Workshops, CIT Workshops 2008	en
dc.identifier.doi	10.1109/CIT.2008.Workshops.118	en
dc.identifier.spage	43	en
dc.identifier.epage	48	en