A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

Different clustering approaches consider different aspects of quality including accuracy of cluster formation, speed of execution, and resource requirements. A major issue in data clustering is to consider the density of clusters and their structure. A challenge is to relax any assumption on the clusters shape such as sphericity, which is common in most partitioning approaches such as K‐means. In this article, with the help of the coalitional game theory and the concept of geodesic distance calculation, a density‐based shape‐independent clustering approach is proposed. In addition to the emphasis on the application of game theory, we also pay attention to the relative neighbourhood of data, which is depicted using geodesic distance. Geodesic distance is a well‐known measure for finding the manifold of data. The new idea supposes to discover any embedded structure of data and avoid finding only spherical clusters. The proposed approach is evaluated on a number of standard University of California, Irvine (UCI) datasets, and the results show the effectiveness of the proposed approach in comparison with some other approaches.