Density-Based Clustering for Adaptive Density Variation

Cluster analysis plays a crucial role in data mining and knowledge discovery. Although many researchers have investigated clustering algorithms over the past few decades, most of the well-known algorithms have shortcomings when dealing with clusters of arbitrary shapes and varying sizes and in the presence of noise and outliers. Density-based methods partially solve these issues but fail to discover clusters with varying densities. In this paper, we propose a novel Density-Based clustering algorithm for Adaptive Density Variation (DBADV), which is based on the classic clustering algorithm DBSCAN. To address the problem of density variation, we define the local density information, which not only reflects the individual property of each object but also describes the density distribution of clusters, and finds the adaptive search range of each object by collecting information from its neighbors. Moreover, we design a new metric to obtain the mutual nearest neighbors of each object to better detect the objects around the boundaries between clusters. We show the effectiveness of our method in extensive experiments on synthetic and realworld data sets, which demonstrate that the performance of the proposed algorithm DBADV is superior to other competitive clustering algorithms.