Detecting unusual pattern with labeled data in two-stage

Authors:
Jincheng Li;Qing He;Zhongzhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate University of Chinese Academy of Sciences, Bei ...;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate University of Chinese Academy of Sciences, Bei ...;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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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 is 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 other pieces 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 maximum 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.