Possibilistic c-Means Clustering Algorithms Based on Adaptive Nearest Neighbors

Possibility C-Means Clustering (PCM) removes the constraint of FCM on membership degree. However, the traditional PCM algorithm tends to encounter coincidence cluster problems during clustering, and its performance is poor when dealing with unbalanced data. To solve the problem of PCM algorithm, this paper proposes a novel algorithm called Possibilistic c-Means Clustering Algorithm Based on Adaptive Nearest Neighbors (PCM-AN). Integrating the CAN model, PCMAN introduces robustness weights to the compatibility degree between sample points and clustering centers, enhancing the local robustness of the model. Utilizing the sparse structural features of adaptive neighbors, PCM-AN emphasizes the divisibility between distinct data clusters. Consequently, PCM-AN algorithm excels in highlighting the relationship within data structures and enhancing separability among various data clusters. In this paper, an artificial two-dimensional dataset, UCI standard test datasets, and image datasets are used for experiments. The experimental results demonstrate that the clustering performance of PCM-AN algorithm has obvious advantages over other improved PCM algorithms.