چکیده (انگلیسی):

Clustering is recognized as sigificant technique for analysing data and concentric effort has been taken
in different domains comprises of recognition of pattern, statistical analysis and data mining for
decades. Subspace clustering is developed from the group of cluster objects from all subspaces of a
dataset. During clustering of objects involing higher dimension, the accuracy and effectiveness of
traditional clustering algorithms are very poor, because data objects may present in different clusters
involving different subspaces of differing level of dimensions. To address the above issue, a new
technique termed Difference Subspace and Opportunistic Clustering (DSOC) model is presented for
high dimensional data to improve the accuracy during the search process and also considers the
problem of accuracy in clustering the high dimensional data. Methods for obtaining subspace and
designing clustering model for DSOC are specified and demonstrated, where the subspace identify the
possibility of each cluster center with the detection of attackers based on multiple locations and
estimation points with derived centroid points. Through comprehensive mathematical analysis, we
show that DSOC improves the accuracy in high dimensional data with efficient cluster validation
obtained using different subspace and opportunistic algorithm. As validated by extensive experiments
on CORTINA and Ski Resort Data Set datasets DSOC produces high quality clusters by detecting the
attackers and the efficiency of DSOC outperforms previous works.