Title :
Detecting Unusual Pattern with Labeled Data in Two-Stage
Author :
Li, Jincheng ; He, Qing ; Shi, Zhongzhi
Author_Institution :
Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci., Beijing, China
Abstract :
More analysis has been done to discover the meaningful unusual patterns which may mean fraud or anomaly. In this paper, a two-stage approach considering the labeled data´s proposed to discover meaningful unusual observation, without the goal of classifying. We firstly apply hyper surface classification (HSC) algorithm to gain a separating hyper surface which includes several pieces. Observation in the sparse piece is viewed as the unusual pattern. For therpieces with local density, we construct a weighted graph for it and search the minimum spanning tree (MST), then detect further by cutting off several edges with the aximum weight. Combining the advantages of the two stages, a process of subdividing is proposed to consider the domain knowledge. Experimental results show that our approach can detect unusual pattern effectively together with other hidden valuable knowledge.
Keywords :
data mining; learning (artificial intelligence); pattern classification; trees (mathematics); domain knowledge; hyper surface classification algorithm; labeled data; meaningful unusual pattern discovering; minimum spanning tree; supervised learning; two-stage approach; unusual pattern detection; weighted graph; Classification algorithms; Computers; Credit cards; Fuzzy systems; Information analysis; Information processing; Laboratories; Pattern analysis; Predictive models; Supervised learning;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
DOI :
10.1109/FSKD.2009.318
