Ensemble-Based Anomaly Detetction using Cooperative Learning


R.F. Kashef ;
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:43-55, 2018.


Using the same process and functionality to solve both clustering and outlier discovery is highly desired. Such integration will be of great benefit to discover outliers in data and consequently obtain better clustering results after eliminating the set of outliers. It is known that the capability of discovering outliers using clustering-based techniques is mainly based on the quality of the adopted clustering. In this paper, a novel Cooperative Clustering Outlier Detection (CCOD) algorithm is presented. It involves multiple clustering techniques; the goal of the cooperative approach is to discover those outliers that are not detected by the single clustering-based outlier detection approaches using the methodology of cooperation. Undertaken experimental results show that the detection accuracy of the cooperative technique is better than that of the typical clustering-based FindCBLOF method over a number of artificial, gene expression and text document datasets.

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