The Probabilistic Growing Cell Structures Algorithm

Vlassis, N; Dimopoulos, A; Papakonstantinou, G

dc.contributor.author	Vlassis, N	en
dc.contributor.author	Dimopoulos, A	en
dc.contributor.author	Papakonstantinou, G	en
dc.date.accessioned	2014-03-01T02:48:29Z
dc.date.available	2014-03-01T02:48:29Z
dc.date.issued	1997	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33858
dc.subject	k-means clustering	en
dc.subject	Mobile Robot	en
dc.subject	Parametric Model	en
dc.subject	Probability Density Function	en
dc.subject	Recursive Estimation	en
dc.title	The Probabilistic Growing Cell Structures Algorithm	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/BFb0020228	en
heal.identifier.secondary	http://dx.doi.org/10.1007/BFb0020228	en
heal.publicationDate	1997	en
heal.abstract	The growing cell structures (GCS) algorithm is an adaptive k-means clustering algorithm in which new clusters are added dynamically to produce a Dirichlet tessellation of the input space. In this paper we extend the non-parametric model of the GCS into a probabilistic one, assuming that samples are distributed in each cluster according to a multi-variate normal probability density function. We	en
heal.journalName	Int. Conference on Artificial Neural Networks	en
dc.identifier.doi	10.1007/BFb0020228	en