Extreme Anomalous Score Clustering Algorithm (original) (raw)

Proceedings of the 2017 International Conference on Information Technology - ICIT 2017, 2017

Abstract

A clustering algorithm usually detects outliers as an aftermath of partitioning data points in a finite dimensional continuous dataset such as AGNES, k-means, and DBSCAN. This research makes use of the extreme anomalous score which represents the outlierness of a data point based on the largest radius of a ball containing only that data point. The new clustering algorithm based on this score is proposed called the extreme anomalous score clustering algorithm (ESC). It searches for a cluster representative by combining two data points which are placed with the smallest extreme anomalous score. Then all extreme anomalous scores are updated and the algorithm stops when it reaches the number of clusters defined by a user. Otherwise, it continues to combine two data points having the smallest extreme anomalous scores. The experimental results on three groups of simulated datasets report the superior performance of ESC over AGNES, k-means, and DBSCAN based on the silhouette measurement and the homogeneity measurement.

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