Special Issue Article Open Access

An Improved C-PCA Technique to Detect Outliers Using Online Oversampling Approach

Abstract

Outlier detection is the process of identifying unusual behavior. It is widely used in data mining, for example, to identify customer behavioral change, fraud and manufacturing flaws. In recent years many researchers had proposed several concepts to obtain the optimal result in detecting the anomalies. But the process of PCA made it challenging due to its computations. In order to overcome the computational complexity, online oversampling PCA has been used. The algorithm enables quick Online updating of the principal directions for the effective computation and satisfying the online detecting demand and also oversampling will improve the impact of outliers which leads to accurate detection of outliers. Experimental results show that this method is effective in computation time and need less memory requirements also clustering technique is added to it for optimization.

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