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GROUP OUTLIER FACTOR: A NEW SCORE USING SELF-ORGANISING MAP FOR GROUP-OUTLIER AND NOVELTY DETECTION

AMINE CHAIBI

LIPN-UMR 7030, University of Paris 13, Sorbonne Paris City – CNRS, 99, av. J-B Clément — F-93430 Villetaneuse, France

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MUSTAPHA LEBBAH

LIPN-UMR 7030, University of Paris 13, Sorbonne Paris City – CNRS, 99, av. J-B Clément — F-93430 Villetaneuse, France

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, and

HANANE AZZAG

LIPN-UMR 7030, University of Paris 13, Sorbonne Paris City – CNRS, 99, av. J-B Clément — F-93430 Villetaneuse, France

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https://doi.org/10.1142/S1469026813500107Cited by:0 (Source: Crossref)

Abstract

This paper describe a new concept of "cluster outlier-ness". In order to quantify it, we propose a relative isolation score named group outlier factor (GOF). GOF is a score, which is computed during a clustering process using self-organizing maps. The main difference between GOF and existing methods is that, being an outlier is not associated to a single pattern but to a cluster. Thus, an outlier factor (OF) with respect to each cluster is computed for each new sample and compared to the GOF score associated for each cluster. OF is used as a novelty detection classifier. This approach allows to identify meaningful outlier-clusters and detects novel patterns that previous approaches could not find. Experimental results and comparison studies show that the use of GOF sensibly improves the results in term of cluster-outlier and novelty detection.

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