Adaptive density-based clustering algorithm with shared KNN conflict game

Abstract

Recently, density-based clustering algorithms have been garnering considerable attention in the unsupervised learning field as they can identify arbitrary cluster shapes. However, classical density-based algorithms are non-backtracking such as density-based spatial clustering of applications with noise (DBSCAN) and its improved variants. Therefore, the allocation of samples cannot be optimised during the clustering process. An incorrect division can cause subsequent errors. Furthermore, when the density is unevenly distributed, the globally uniform parameters will inevitably lead to catastrophic error consequences. To address these limitations, this study proposes an adaptive density-based clustering algorithm using the shared k-nearest neighbours (SKNN) conflict game (DC-SKCG). A local density-based adaptive cut-off distance-setting method is designed for the DC-SKCG, and a conflict game method based on SKNN is proposed to optimise the clustering process. Moreover, an automatic fusion mechanism for redundant high-density core regions is presented to (1) reduce the parameter sensitivity of the algorithm and (2) improve the parameter tolerance capacity. A series of experiments are conducted using various datasets demonstrate that DC-SKCG offers higher accuracy and robustness in most cases than state-of-the-art methods.