Developing of a New Hybrid Clustering Algorithm Based on Density

Abstract

Clustering is one of the fundamental techniques of data mining that is used for dataset analysis. Clustering algorithms group available data based on similarity or distance measures. Two important clustering methods used in the literature are hierarchical and density based methods. A lot of algorithms have been developed based on these two concepts separately. Birch and its extensions are samples of hierarchical based methods. DBSCAN and its extensions are samples of density based methods. In this paper, a new algorithm is proposed to use both concepts together to achieve an acceptable speed and results, simultaneously. At first, it tries to make clusters using a hierarchical method. If it decides to make a new cluster, then the algorithm checks for density. In this manner, it tries to postpone splitting the clusters. To show the effect of the proposed algorithm, some evaluations are performed on some synthetic and real datasets which show some improvements over related works.