DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores

Numerous clustering algorithms become invalid when data classes are unbalanced and close to each other, or clusters are arbitrary shapes and have different densities. A novel region density based clustering algorithm, DEDIC, is proposed to meet this challenge. It is based on recognizing dense regions using mutual nearest neighbors and estimating region density of a cluster by computing maximum of the directly-reachable distances among core points within the cluster. DEDIC is superior to the popular density-based clustering algorithm DBSCAN in two aspects. First, since there is only one parameter (choice of k nearest neighbors), the algorithm complexity is reduced, and second, an improved ability to handle clusters with large density variations. The superiority of DEDIC is demonstrated on several artificial and real-world datasets with respect to nine known clustering approaches.