An efficient Support Vector Clustering with combined core outlier and boundary value for pre-processing of data

The performance of support vector clustering suffered Due to noisy data. The pre-processing of data play important role in support vector cluster. In support vector clustering the mapping of data from one sphere to another sphere found some unwanted behaviour of data, these behaviour are boundary point, core and outlier. These data point degrade the performance and efficiency of support vector clustering. For the reduction of core, outlier and boundary value, we combined all dissimilar data and form COB model and data passes through genetic algorithm for collective collection of COB and reduce the COB value in data pre-processing phase. After reduction of COB support vector clustering are applied. Our empirical evaluation shows that our method is better than incremental support vector clustering and SSN-SVC.