Data Mining Methods for Anomaly Detection KDD-2005 Workshop Report Dragos Margineantu, Stephen Bay, Philip Chan, and Terran Lane   View Paper (PDF)   Return to December 2005, Volume 7, Issue 2 Abstract For many applications, data mining systems are required to detect anomalous (abnormal, unmodeled, or unexpected) observations. This has so far proven to be a difficult challenge because anomalies are usually considered to be "non-normal" observations, where "normality" is typically defined by very complex concepts. Because of these and other reasons, there are no standard and principled approaches for anomaly detection, yet, and the data mining processes that have led to successful solutions include most of the times ad-hoc (algorithmic, design, and implementation) decisions that incorporate prior or commonsense knowledge about the tasks that are addressed. Consequently, we considered that it would be beneficial for both researchers and practitioners interested in anomaly detection and data mining, to organize workshop that would bring together people interested in this topic. We considered that the International Conference on Knowledge Discovery and Data Mining would be a good venue for such a workshop because of the diversity of interests, backgrounds, and problems that motivate people to attend the conference. This paper describes the workshop on "Data Mining Methods for Anomaly Detection" - a one day event held in conjunction with KDD-2005 in Chicago, on August 21, 2005. ©2006 Association for Computing Machinery