A Probabilistic Estimation of Cluster Region Prone to Inter Cluster Data Movement - Rajee, AM; Sagayaraj Francis, F (International Journal of Computer Sciences and Engineering, 2014)
Data clustering is an unsupervised learning methodology. We consider the problem of dealing with unclustered information to the already existing clustering setup. The new entrée may cause movement of data points between clusters, thereby altering the dynamics of the clustering system. With this scenario, this paper attempts to predict the region in the cluster which will facilitate such inter cluster data movement. A probabilistic model was built which will estimate the region, which has higher chance for enabling the data objects to move in and out of the cluster. Experimental studies were made with multiple instances of synthetic two dimensional data sets. The observed values were compared with the predicted values and the results displayed improved accuracy of the probabilistic model.