Adaptive kernel fuzzy C-Means clustering algorithm based on cluster structure

Abstract

The well-known Fuzzy C-Means (FCM) algorithm and its modified clustering derivatives have been widely applied in various fields. However, previous studies have focused on the yield of correctly clustered data, and few have addressed the alignment of extracted influential areas of clusters to natural cluster structure. Various clustering algorithms present diverse characteristics in cluster structure detection due to the different clustering principles involved. For example, Mahalanobis distance-based FCM algorithms effectively detect the influential direction of each cluster, while kernel-based FCM algorithms provide an interface for adjusting the influential range. Combining the advantages of these previous algorithms, the Adaptive Kernel Fuzzy C-Means (AKFCM) algorithm based on cluster structure is proposed in this paper. The AKFCM algorithm can effectively detect the influential direction and adjust the influential range of each cluster with adaptive kernelization. By applying the previous and AKFCM algorithms to both synthetic and real-world datasets, the proposed algorithm is proven to achieve better performance not only in clustering accuracy but also in the extraction of reasonable influential areas. The proposed algorithm could be helpful for clustering datasets composed of clusters with different directions and ranges in structure.