Belief-peaks clustering based on fuzzy label propagation

For unsupervised learning, we propose a new clustering method which incorporates belief peaks into a linear label propagation strategy. The proposed method aims to reveal the data structure by finding out the exact number of clusters and deriving a fuzzy partition. Firstly, the cluster centers and outliers can be identified by the improved belief metric, which makes use of the whole data distribution information so as to correctly highlight the cluster centers without the limitation of massive neighbor points. Secondly, an informative initial fuzzy cluster assignment for each remaining point is created by considering the distances between its neighbors and each cluster center, then the fuzzy label of each point will be iteratively updated by absorbing its neighbors’ label information until the fuzzy partition is stable. The label propagation assignment strategy provides a valuable alternative technique with explicit convergence and linear complexity in the field of belief-peaks clustering. The effectiveness of the proposed method is tested on seven commonly used real-world datasets from the UCI Machine Learning Repository, and seven synthetic datasets in the domain of data clustering. Comparing with several state-of-the-art clustering methods, the experiments reveal that the proposed method enhanced the clustering results in terms of the exact numbers of clusters and the Adjusted Rand Index. Further, the parameter analysis experiments validate the robustness to the two tunable parameters in the proposed method.