Outlier Detection Using Ball Descriptions with Adjustable Metric

Sometimes novel or outlier data has to be detected. The outliers may indicate some interesting rare event, or they should be disregarded because they cannot be reliably processed further. In the ideal case that the objects are represented by very good features, the genuine data forms a compact cluster and a good outlier measure is the distance to the cluster center. This paper proposes three new formulations to find a good cluster center together with an optimized lp-distance measure. Experiments show that for some real world datasets very good classification results are obtained and that, more specifically, the l1-distance is particularly suited for datasets containing discrete feature values.

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