The minimum spanning tree-(MST-) based clustering method can identify clusters of arbitrary shape by removing inconsistent edges. The definition of the inconsistent edges is a major issue that has to be addressed in all MST-based clustering algorithms. In this paper, we propose a novel MST-based clustering algorithm through the cluster center initialization algorithm, called cciMST. First, in order to capture the intrinsic structure of the data sets, we propose the cluster center initialization algorithm based on geodesic distance and dual densities of the points. Second, we propose and demonstrate that the inconsistent edge is located on the shortest path between the cluster centers, so we can find the inconsistent edge with the length of the edges as well as the densities of their endpoints on the shortest path. Correspondingly, we obtain two groups of clustering results. Third, we propose a novel intercluster separation by computing the distance between the points at the intersection of clusters. Furthermore, we propose a new internal clustering validation measure to select the best clustering result. The experimental results on the synthetic data sets, real data sets, and image data sets demonstrate the good performance of the proposed MST-based method.

Most of traditional MST-based (Minimum spanning tree) clustering algorithms cluster by removing the inconsistent edge. The performances of these algorithms are influenced by the shape of clusters. To address this issue, we proposed a novel cluster algorithm based on the combination of MST and cluster centers (iGMST). Firstly, the cluster centers are determined by the Geodesic distance between vertex pair in the MST. Then, the inconsistent edge is defined along the path between cluster center pair. The experimental results on the synthetic and real data sets show that iGMST is better than k-means++, hierarchical clustering, and spectral clustering. Besides, iGMST can discover clusters of different shape steadily.