LOADED: Link-Based Outlier and Anomaly Detection in Evolving Data Sets

Authors:
Amol Ghoting;Matthew Eric Otey;Srinivasan Parthasarathy
Affiliations:
The Ohio State University;The Ohio State University;The Ohio State University
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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Abstract

In this paper, we present LOADED, an algorithm for outlier detection in evolving data sets containing both continuous and categorical attributes. LOADED is a tunable algorithm, wherein one can trade off computation for accuracy so that domain-specific response times are achieved. Experimental results show that LOADED provides very good detection and false positive rates, which are several times better than those of existing distance-based schemes.