Properties Of Potential Function- Based Clustering Algorithms

dc.contributor.author	Nicolau, Viorel
dc.contributor.author	Puşcaşu, Gheorghe
dc.contributor.author	Popa, Rustem
dc.date.accessioned	2015-12-09T11:08:15Z
dc.date.available	2015-12-09T11:08:15Z
dc.date.issued	2000
dc.identifier.issn	1221-454X
dc.identifier.uri	http://10.11.10.50/xmlui/handle/123456789/3704
dc.description	The Annals of "Dunarea de Jos" University of Galati	en_US
dc.description.abstract	The clustering algorithms based on potential functions are capable of clustering a set of data, making no implicit assumptions on the cluster shapes and without knowing in advance the number of clusters. They are similarity-based type clustering algorithms and do not use any prototype vectors of the clusters. In this paper, some properties of these algorithms are studied: points arrangement tendency, constant potential surface, cluster shapes and robustness to noise.	en_US
dc.language.iso	en	en_US
dc.publisher	"Dunarea de Jos" University of Galati	en_US
dc.subject	potential function	en_US
dc.subject	clustering algorithm	en_US
dc.subject	measure of similarity	en_US
dc.title	Properties Of Potential Function- Based Clustering Algorithms	en_US
dc.type	Article	en_US