A combinational hierarchical clustering algorithm on the basis of density-based methods

Clustering is one of the most important field of data mining that aims to divide data into meaningful subsets which are called clusters. This technique involves the process of finding natural groupings in the data set based on the similarities and di similarities which a little or no information about data are available. Over the decades, many clustering algorithms are created in different approaches or a combination of them. In this paper, an algorithm based on density and hierarchical approaches is presented. DBSCAN is one of the algorithms presented in the density-based approach. This algorithm requires two parameters that its determination is a great challenge. In the proposed method, DBSCAN algorithm parameters can be set without user involvement, so that potential clusters are found automatically. The clusters which are so close to each other are merged together until the quality of the final clusters to be enhanced properly. Thus, clusters could be more accurate and high quality. Finally, in order to test the new proposed algorithm, the real dataset in the UCI machine learning repository was used. The results indicate that the new algorithm is more efficient and accurate, and its speed is better than previous methods.