Hidden features extraction using Independent Component Analysis for improved alert clustering

Abstract

Feature extraction plays an important role in reducing the computational complexity and increasing the accuracy. Independent Component Analysis (ICA) is an effective feature extraction technique for disclosing hidden factors that underlying mixed samples of random variable measurements. The computation basic of ICA presupposes the mutual statistical independent of the non-Gaussian source signals. In this paper, we apply ICA algorithm as hidden features extraction to enhance the alert clustering performance. We tested the ICA against k-means, EM and Hierarchies unsupervised clustering algorithms to find the optimal performance of the clustering. The experimental results show that ICA effectively improves clustering accuracy.