MPC: A Novel Internal Clustering Validity Index Based on Midpoint-Involved Distance

As one of the most import machine learning technique, clustering is widely used in many data classification areas. Due to the unsupervised learning feature, the quality of the clustering results needed to be evaluated. In this paper, the MPdist (midpoint-involved distance) based on the midpoint of centers between two clusters is firstly defined to measure the inter-cluster separation. Then, the MPC (MPdist based clustering validity index), a novel internal clustering validity index based on the combination of the new defined inner-cluster compactness and the inter-cluster separation, is proposed to effectively evaluate the validity of the clustering results of many clustering algorithms. Experimental results on testing many types of datasets have demonstrated that the MPC index proposed in this paper is able to quickly handle datasets like spherical datasets, non-spherical datasets and real large-scale datasets.