Unusual Pattern Detection Based on Hyper Surface and Minimum Spanning Tree

摘要：

More analysis has been done to discover the meaningful unusual patterns which may mean fraud or anomaly. In this paper, a novel unsupervised approach for discovering meaningful unusual observations is proposed. We firstly apply an unsupervised version of Hyper Surface Classification (HSC) algorithm to gain the separating hyper surface. It needs no domain knowledge but can not discover the local unusual pattern. To solve this problem, we additionally search the Minimum Spanning Tree (MST). Given the domain knowledge, a process of subdividing is proposed to detect unusual pattern in each Minimum Spanning Tree. Experimental results show that our approach can detect unusual patterns effectively, even some of which are overlooked by using the traditional clustering and outlier detection algorithms.

作者: Qing He Jincheng Li Weizhong Zhao Zhongzhi Shi

作者单位: The Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese A The Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese A

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)

会议地点: 珠海、澳门

会议语种:英文

页码: 1093-1098

在线出版日期: 2009-06-22（万方平台首次上网日期，不代表论文的发表时间）