Naldi, Murilo Coelho, and Campello, Ricardo José Gabrielli Barreto (2012) Combining information from distributed evolutionary k-means. In: Brazilian Symposium on Neural Networks. pp. 43-48. From: SBRN 2012: Brazilian Symposium on Neural Networks, 20-25 October 2012, Curitiba, Brazil.

PDF (Published Version) - Published Version Restricted to Repository staff only

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.

Item ID:	46787
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4673-2641-4
Keywords:	k-means; distributed data sets
Funders:	CNPq, Brazil, FAPESP
Date Deposited:	02 May 2017 03:40
FoR Codes:	01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
	More Statistics