A novel validity index with dynamic cut-off for determining true clusters

In a multi-surveillance environment, voluminous data is generated over a period of time. Data analysis for summarization and conclusion has paved a way for the need of an efficient clusterization. Clustering, an unsupervised way of learning about data aims at defining clusters. Validation of clusters formed indicates the trueness of the clusters. In this paper, a novel validation technique with dynamic termination of clustering process has been proposed to obtain true clusters. In the validation process, the validity index is based on both global cluster proximity relationship and local proximity relationship. The validity index is computed for validating the available clusters using 'within-cluster sum-of-squares', 'between-cluster sum-of-squares', 'total-sum-of-squares', Intra-cluster distances' and 'inter-cluster distances'. The ratio between two consecutive validity indices is the extent of variation which specifies the cut-off point. Cut-off terminates the clustering process dynamically indicating the number of clusters and validates the obtained clusters. The proposed method is tested on several real and synthetic data sets. Comparisons with the existing methods demonstrate the efficiency of the proposed method in detecting true clusters. (C) 2015 Elsevier Ltd. All rights reserved.