A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique

Abstract

In hybrid clustering, several basic clustering is first generated and then for the clustering aggregation, a function is used in order to create a final clustering that is similar to all the basic clustering as much as possible. The input of this function is all basic clustering and its output is a clustering called clustering agreement. However, this claim is correct if some conditions are met. This study has provided a hybrid clustering method. This study has used the basic k-means clustering method as a basic cluster. Also, this study has increased the diversity of consensus by adopting some measures. Here, the aggregation process of the basic clusters is done by the meta-clustering technique, where the primary clusters are re-clustered to form the final clusters. The proposed hybrid clustering method has the advantages of k-means, its high speed, as well as it does not have its major weaknesses, the inability to detect non-spherical and non-uniform clusters. In the empirical studies, we have evaluated the proposed hybrid clustering method with other up-to-date and robust clustering methods on the different datasets and compared them. According to the simulation results, the proposed hybrid clustering method is stronger than other clustering methods.