A Novel Density-Based Spatial Clustering of Application with Noise Method for Data Clustering

Abstract

The traditional methods are easy to generate a large number of fake samples or data loss when classifying unbalanced data. Therefore, this paper proposes a novel DBSCAN (density-based spatial clustering of application with noise) for data clustering. The density-based DBSCAN clustering decomposition algorithm is applied to most classes of unbalanced data sets, which reduces the advantage of most class samples without data loss. The algorithm uses different distance measurements for disordered and ordered classification data, and assigns corresponding weights with average entropy. The experimental results show that the new algorithm has better clustering effect than other advanced clustering algorithms on both artificial and real data sets.