Authors: Qiang Jiang; Akiko Campbell; Guanting Tang; Jian Pei
Addresses: Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada ' Pacific Blue Cross, 4250 Canada Way, Burnaby, BC V5G 4W6, Canada ' Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada ' Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Abstract: Relationship management is critical in business. Particularly, it is important to detect abnormal relationships, such as fraudulent relationships between service providers and consumers. Surprisingly, in the literature there is no systematic study on detecting relationship outliers. Particularly, no existing methods can detect and handle relationship outliers between groups and individuals in groups. In this paper, we tackle this important problem by developing a simple yet effective model. The major novelty is that we identify two types of outliers and devise efficient detection algorithms. Our experiments on both real data and synthetic data confirm the effectiveness, efficiency and scalability of our approach. The techniques reported in this paper have been in production in a large scale business application.
Keywords: outlier detection; multi-level relationships; iceberg cubing; data mining; business intelligence; relationship management; abnormal relationships; relationship outliers; fraudulent relationships.
International Journal of Business Intelligence and Data Mining, 2012 Vol.7 No.4, pp.253 - 273
Available online: 27 Jan 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article