Given a set of outlier examples, example-based outlier mining finds more outliers from the dataset that exhibit the similar outlier qualities to the given outlier examples.
Published in Chapter:
Outlying Subspace Detection for High-Dimensional Data
Ji Zhang (CSIRO Tasmanian ICT Centre, Australia), Qigang Gao (Dalhousie University, Canada), and Hai Wang (Saint Mary’s University, Canada)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-60566-242-8.ch059
Abstract
Knowledge discovery in databases, commonly referred to as data mining, has attracted enormous research efforts from different domains such as databases, statistics, artificial intelligence, data visualization, and so forth in the past decade. Most of the research work in data mining such as clustering, association rules mining, and classification focus on discovering large patterns from databases (Ramaswamy, Rastogi, & Shim, 2000). Yet, it is also important to explore the small patterns in databases that carry valuable information about the interesting abnormalities. Outlier detection is a research problem in small-pattern mining in databases. It aims at finding a specific number of objects that are considerably dissimilar, exceptional, and inconsistent with respect to the majority records in an input database. Numerous research work in outlier detection has been proposed such as the distribution-based methods (Barnett & Lewis, 1994; Hawkins, 1980), the distance-based methods (Angiulli & Pizzuti, 2002; Knorr & Ng, 1998, 1999; Ramaswamy et al.; Wang, Zhang, & Wang, 2005), the density-based methods (Breuning, Kriegel, Ng, & Sander, 2000; Jin, Tung, & Han, 2001; Tang, Chen, Fu, & Cheung, 2002), and the clustering-based methods (Agrawal, Gehrke, Gunopulos, & Raghavan, 1998; Ester, Kriegel, Sander, & Xu, 1996; Hinneburg & Keim, 1998; Ng & Han, 1994; Sheikholeslami, Chatterjee, & Zhang, 1999; J. Zhang, Hsu, & Lee, 2005; T. Zhang, Ramakrishnan, & Livny, 1996).