DH-Kmeans: An Improved K-means Clustering Algorithm based on Dynamic Initial Cluster Center Determination and Hierarchical Clustering

As a classical clustering algorithm, the K-means is widely applied and studied in the information retrieval. However, the performance of this algorithm is sensitive to the selection of initial clustering clusters. Improper selection of these centers will trap the algorithm into the problem of local optimum. Meanwhile, this algorithm cannot process the non-convex datasets effectively. In order to ease these problems, this paper proposes the DHKmeans, an improved K-means based on the dynamic initial clustering center determination and the hierarchical clustering. In the DH-Kmeans, a comprehensive measure which is based on the density parameter and the cluster distance is firstly designed to dynamically determine the initial clustering centers. By this, the problem of local optimum is avoided. Then, the Agglomerative Hierarchical Clustering (AHC) is integrated into the iteration procedures of the DH-Kmeans. In the iteration procedures, the similar small clusters are merged gradually to endow the DH-Kmeans with the ability of precisely processing more types of datasets. Experimental results on testing 25 artificial datasets and six real datasets have demonstrated that the DH-Kmeans is fast, stable, and accurate in data clustering.