A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS
International Journal of Pattern Recognition and Artificial IntelligencePublished 24 March 2011
Sandro Vega-Pons, José Ruíz-Shulcloper
Citations610
SJR quartileQ3
SJR score0.27
SNIP0.40
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TL;DR
An overview of clustering ensemble methods that can be very useful for the community of clusters practitioners is presented and a taxonomy of these techniques is presented to illustrate some important applications.
Abstract
Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.
Keywords
Computer Science
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