This paper presents an improved cluster validation scheme called two phase cluster validation (TPCV) and aims to estimate the inter closeness and inter separation among the clusters in the cluster set of unsupervised clustering schemes based on probability measure for validating the cluster quality without prior identification. First phase, the TPCV computes the representative cluster centroid of each individual cluster in the cluster set based on standard mean operation and then it estimates the probability of inter closeness of each cluster with other clusters in the cluster set based on cluster centroid. Next phase, it calculates the probability of separation among the clusters in the cluster set based on cluster centroid by distance measure. Experimental results show that the TPCV scheme is simple and effective to estimate the cluster quality by measuring the probability of closeness and separation between the clusters in the result of unsupervised clustering scheme.
This paper proposes a novel clustering methodology which undeniably manages to offer results with a higher inter-cluster inertia for a better clustering. The advantage obtained with this methodology is due to an algorithm that showed beforehand its efficiency in clustering exercises, MC-DBSCAN, which is associated to an iterative process with a potential of auto-adjustment of the weights of the pertinent criteria that allows the reclassification of objects of the two closest clusters through each iteration, as well as the aptitude of the auto-evaluation of the precision of the clustering during the clustering process. This work conducts the experiments using the well-known benchmark, 'Seismic', 'Landform-Identification' and 'Image Segmentation', to compare the performance of the proposed methodology with other algorithms (K-means, EM, CURE and MC-DBSCAN). The experimental results demonstrate that the proposed solution has good quality of clustering results.
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