Automatic PAM Clustering Algorithm for Outlier Detection

In the past decade there has been intensive research on clustering algorithms for outlier detection, which has the advantage of simple modeling and effectiveness. In this paper, we propose an automatic k-means algorithm for outlier detection. The proposed methodology comprises two phases, clustering and finding outlying score. During clustering phase we automatically determine the number of clusters by combining k-means clustering algorithm and a specific cluster validation metric, which is vital to find a clustering solution that best fits the given data set, especially for k-means clustering algorithm. During finding outlier scores phase we decide outlying score of data instance corresponding to the cluster structure. Experiments on different datasets show that the proposed algorithm has higher detection rate go with lower false alarm rate comparing with the state of art outlier detection techniques, and it can be an effective solution for detecting outliers.

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