Aggregation-based self-supervised evidential clustering for imbalanced data

Abstract

Clustering, as a fusion process, involves aggregating similar objects and isolating dissimilar ones, independent of any prior information. Recently, evidential clustering has gained popularity due to its ability to characterize the uncertainty and imprecision of data distribution. However, it remains a major bottleneck of existing evidential clustering methods for clustering imbalanced data, as they cannot effectively detect small clusters (with a few objects). In this paper, we propose a new aggregation-based self-supervised evidential clustering (ASEC) method for dealing with such issues based on the theory of belief functions. Specifically, a cluster density-based aggregation rule is designed first to generate multiple sub-clusters and then fuse them into new singleton clusters, which can effectively detect small clusters of imbalanced data. The new singleton clusters obtained by the aggregation rule serve as prior knowledge. Then, a self-supervised evidential partition rule is developed to fuse the remaining objects into new clusters according to prior knowledge and the $K$-nearest neighbors (KNNs) technique. In this process, the objects in the overlapping zones of clusters are usually hard to classify, and they are assigned to new meta-clusters to reduce the risk of error. Experiments on several imbalanced datasets demonstrate the effectiveness of ASEC compared to related methods.