Combining information from distributed evolutionary k-means

Murilo Coelho Naldi*, Ricardo Jose Gabrielli Barreto Campello

*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

One of the challenges for clustering resides in dealing with huge amounts of data, which causes the need for distribution of large data sets in separate repositories. However, most clustering techniques require the data to be centralized. One of them, the k-means, has been elected one of the most influential data mining algorithms. Although exact distributed versions of the k-means algorithm have been proposed, the algorithm is still sensitive to the selection of the initial cluster prototypes and requires that the number of clusters be specified in advance. This work tackles the problem of generating an approximated model for distributed clustering, based on k-means, for scenarios where the number of clusters of the distributed data is unknown. We propose a collection of algorithms that generate and select k-means clustering for each distributed subset of the data and combine them afterwards. The variants of the algorithm are compared from two perspectives: the theoretical one, through asymptotic complexity analyses, and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests.

OriginalsprogEngelsk
TitelProceedings - 2012 Brazilian Conference on Neural Networks, SBRN 2012
ForlagIEEE
Publikationsdato2012
Sider43-48
Artikelnummer6374822
ISBN (Trykt)9780769548234
DOI
StatusUdgivet - 2012
Udgivet eksterntJa
Begivenhed2012 Brazilian Conference on Neural Networks, SBRN 2012 - Curitiba, Parana, Brasilien
Varighed: 20. okt. 201225. okt. 2012

Konference

Konference2012 Brazilian Conference on Neural Networks, SBRN 2012
Land/OmrådeBrasilien
ByCuritiba, Parana
Periode20/10/201225/10/2012
SponsorBrazilian Computer Society (SBC)
NavnProceedings - Brazilian Symposium on Neural Networks, SBRN
ISSN1522-4899

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