International Journal of Data Mining Techniques and Applications Vol:02, December 2013, Pages: 258-265 Integrated Intelligent Research (IIR) 258 Improve the Performance of Clustering Using Combination of Multiple Clustering Algorithms Kommineni Jenni 1 ,Sabahath Khatoon 2 , Sehrish Aqeel 3 1Lecturer , Dept of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia Jenni.k.507@gmail.com, sabakhan_312@yahoo.com, sehrishaqeel.kku@gmail.com 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. Keywords- Clustering, density; pheromone; ant colony algorithm; k-means, hard k-means, fuzzy c-means, Cooperative Hard-Fuzzy; I. INTRODUCTION Clustering can be considered as the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A cluster is a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. A large number of clustering methods [1]-[10] have been developed in many fields, with different definitions of clusters and similarity metrics. It is well known that no clustering method can adequately handle all sorts of cluster structures and properties (e.g. overlapping, shape, size and density). In fact, the cluster structure produced by a clustering method is sometimes an artifact of the method itself that is actually imposed on the data rather than discovered about its true structure. Combining multiple clustering [11]-[15] is considered as an example to further broaden and stimulate new progress in the area of data clustering. Clustering is an important technology of the data mining study, which can effectively discovered by analyzing the data and useful information. It groups data objects into several classes or clusters so that in the same cluster of high similarity among objects, and objects are vary widely in the different cluster [19]. Combining clustering can be classified into two categories based on the level of cooperation between the clustering algorithms; either they cooperate on the intermediate level or at the end- results level. Examples of end-result cooperation are the Ensemble Clustering and the Hybrid Clustering approaches [11]-[15]. Ensemble clustering is based on the idea of combining multiple clusterings of a given dataset X to produce a superior aggregated solution based on aggregation function. Recent ensemble clustering techniques have been shown to be effective in improving the accuracy and stability of standard clustering algorithms. However, an inherent drawback of these techniques is the computational cost of generating and combining multiple clusters of the data. In this paper, we propose a new Cooperative Hard-Fuzzy Clustering (CHFC) model based on obtaining best clustering solutions from the hard k-means (KM) [5] and the fuzzy c-means (FCM) [6] at the intermediate steps based on a cooperative criterion to produce better solutions and to achieve faster convergence to solutions of both