Improve the Performance of Clustering Using Combination of Multiple Clustering Algorithms

Abstract

The ever-increasing availability of textual documents has lead to a growing challenge for information systems to effectively manage and retrieve the information comprised in large collections of texts according to the user�s information needs. There is no clustering method that can adequately handle all sorts of cluster structures and properties (e.g. shape, size, overlapping, and density). Combining multiple clustering methods is an approach to overcome the deficiency of single algorithms and further enhance their performances. A disadvantage of the cluster ensemble is the highly computational load of combing the clustering results especially for large and high dimensional datasets. In this paper we propose a multiclustering algorithm , it is a combination of Cooperative Hard-Fuzzy Clustering model based on intermediate cooperation between the hard k-means (KM) and fuzzy c-means (FCM) to produce better intermediate clusters and ant colony algorithm. This proposed method gives better result than individual clusters.