Evolutionary Rough K-Means Clustering

Abstract

Rough K-means algorithm and its extensions have been useful in situations where clusters do not necessarily have crisp boundaries. Experimentation with the rough K-means algorithm has shown that it provides a reasonable set of lower and upper bounds for a given dataset. Evaluation of clustering obtained from rough K-means using various cluster validity measures has also been promising. However, rough K-means algorithm has not been explicitly shown to provide optimal rough clustering. This paper proposes an evolutionary rough K-means algorithm that minimizes a rough within-group-error. The proposal is different from previous Genetic Algorithms (GAs) based rough clustering, as it combines the efficiency of rough K-means algorithm with the optimization ability of GAs. The evolutionary rough K-means algorithm provides flexibility in terms of the optimization criterion. It can be used for optimizing rough clusters based on different criteria.KeywordsGenetic AlgorithmCluster SchemeInformation GranuleCluster Validity MeasureCrisp BoundaryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.