A Heterogeneous Cluster Ensemble Model for Improving the Stability of Fuzzy Cluster Analysis

Cluster analysis is an important exploratory tool which reveals underlying structures in data and organizes them in clusters (groups) based on their similarities. The fuzzy approach to the clustering problem involves the concept of partial memberships of the instances in the clusters, increasing the flexibility and enhancing the semantics of the generated clusters. Several fuzzy clustering algorithms have been devised like fuzzy c-means (FCM), Gustafson-Kessel, Gath-Geva, kernel-based FCM etc. Although these algorithms do have a myriad of successful applications, each of them has its stability drawbacks related to several factors including the shape and density of clusters, the presence of noise or outliers and the choices about the algorithm's parameters and cluster center initialization. In this paper we are providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis. The key idea of our methodology is the application of different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix. Finally we have experimentally evaluated and compared the accuracy of this methodology.