An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters

Imbalanced data clustering is a challenging problem in machine learning. The main difficulty is caused by the imbalance in both cluster size and data density distribution. To address this problem, we propose a novel clustering algorithm called LDPI based on local-density peaks in this study. First, an initial sub-cluster construction scheme is designed based on a 3-dimensional (3-D) decision graph that can easily detect the initial sub-cluster centers and identify the noise points. Second, a sub-cluster updating strategy is designed, which can automatically identify the false sub-cluster centers and update the initial sub-clusters. Third, a sub-cluster merging scheme is designed, which merges the updated initial sub-clusters into final clusters. Consequently, the proposed algorithm has three advantages: 1) It does not require any input parameters; 2) It can automatically determine the cluster centers and number of clusters; 3) It is suitable for imbalanced datasets and datasets with arbitrary shapes and distributions. The effectiveness of LDPI is demonstrated experimentally and the superiority of LDPI is identified by comparison with 5 state-of-the-art algorithms.

Automated summary

In this study, a novel clustering algorithm LDPI based on local-density peaks is proposed to address the challenging problem of imbalanced data clustering. The algorithm has advantages such as not requiring input parameters, automatically determining cluster centers and number of clusters, and being suitable for imbalanced datasets and datasets with arbitrary shapes and distributions.