Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

Abstract

Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. Here the authors propose a local direction centrality clustering algorithm that copes with heterogeneous density and weak connectivity issues.