A novel clustering algorithm based on fitness proportionate sharing
Xuyang Yan, Abdollah Homaifar, Shabnam Nazmi, Mohammad Razeghi-Jahromi · 2017
Existing clustering techniques primarily rely on prior knowledge about the data, such as the number of clusters and radii. However, in real applications, the number of clusters and the radii of clusters are usually unknown. Therefore, the performance of clustering methods with overlapping data is degraded due to their limitations in finding all cluster centers with uneven density values. Hence, a new clustering algorithm based on fitness proportionate sharing is proposed to map the problem into a multimodal optimization problem. In this paper, clusters are considered as niches, and the individuals with the highest density values of each niche are the cluster centers. Instead of using the traditional sharing strategy, the fitness proportionate sharing strategy is implemented in the identification of niche maxima to overcome the sensitivity of uneven density values of cluster centers. A procedure of niche expansion is employed for the merging of clusters. Simulation results and complexity analysis reveal that the proposed clustering algorithm based on fitness proportionate sharing provides a higher accuracy performance without any prior information.