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With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has become indispensible to build not only a robust and a generalised model for anomaly detection but also to dress the same model with extra features like utmost accuracy and precision. Although the K-means algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an attempt has done to combine K-means algorithm with PCA technique that results in the formation of more closely centred clusters that work more accurately with K-means algorithm .This combination not only provides the great boost to the detection of outliers but also enhances its accuracy and precision.

A Mixture Model of Hubness and PCA for Detection of Projected Outliers