Outlying Subspace Detection for High- dimensional Data

Knowledge discovery in databases, commonly referred to as data mining, has attracted enormous research efforts from different domains such as database, statistics, artificial intelligence, data visualization, etc, in the past decade. Most of the research work in data mining such as clustering, association rules mining and classification focus on discovering the “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 abnormal regularities. 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; Knorr & Ng, 1999; Ramaswamy, Rastogi & Shim, 2000, Wang, Zhang& Wang, 2005), the density-based methods (Breuning, Kriegel, Sander & Xu, 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; Zhang, Whsu & Lee, 2005; Zhang, Ramakrishnan & Livny, 1996).