Detecting outliers in interval data

Authors:
Shuxin Li;Robert Lee;Sheau-Dong Lang
Affiliations:
University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL
Venue:
Proceedings of the 44th annual Southeast regional conference
Year:
2006

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Abstract

Outlier detection has become an important data mining problem in many applications, including customer management and fraud detection. In recent years, many algorithms have been developed for discovering outliers in large databases. However, to our knowledge, no algorithm exists for discovering outliers in interval data. In this paper, we propose an efficient algorithm to detect distance-based outliers in interval data. We perform empirical studies on real and simulated interval datasets to evaluate the effectiveness of our proposed algorithm in identifying meaningful outliers.