Clustering Fusion with Automatic Cluster Number

Most of the real world applications use data clustering techniques for effective data analysis. All clustering techniques have some assumptions on the underlying dataset. We can get accurate clusters if the assumptions hold good. But it is difficult to satisfy all assumptions. Currently, not a single clustering algorithm is available to find all types of cluster shapes and structures. Therefore, an ensemble clustering algorithm is proposed in this paper in order to produce accurate clusters. Moreover, the existing clustering ensemble methods require more number of clusters in advance to produce final clusters. In this paper, we propose a novel method which groups a set of clusters into accurate final clusters to enhance the decision accuracy. This method does not need the number of clusters as input but produces the clusters automatically assuming the no of clusters.