A density-based fuzzy exemplar clustering algorithm

According to the characteristics of density-based clustering and fuzzy clustering, a density-based fuzzy exemplar clustering algorithm is proposed. Firstly, the possibility of data points becoming candidate clustering centers is processed by the density. The higher the density of the data point is, the greater the likelihood for the data point to become a clustering center is. The clustering centers are then selected using the fuzzy method. The final clustering centers are determined by merging the clustering centers. The proposed algorithm has great adaptability, which can deal with clustering problems of different shapes, it can not only automatically determine cluster centers, but also get better results with higher accuracy. It can automatically determine the real clustering centers with good interpretability and there is no need to preset the number of clusters in advance. By combining the advantages of different clustering methods, the effective division of data can be realized. In addition, it has better robustness to number of clusters and initialization, processing clustering problems of different shapes, and dealing with outliers. Experiments on synthetic datasets and UCI datasets show that the proposed algorithm has better clustering performance and wide applicability. © 2020, Editorial Office of Control and Decision. All right reserved.