数据挖掘应用于犯罪集团或恐怖组织社会网络结构分析已经成为公安信息系统领域的研究热点,国内外在分析犯罪和恐怖组织之间的内在规律方面的研究工作亟待深入.与一般的数据挖掘技术相比,图能够表达更加丰富的语义,基于图数据挖掘技术应用于犯罪规律研究是一种新兴的研究方法.为了挖掘犯罪规律和频繁出现的核心成员,首先提出了基于图数据挖掘的相关理论,然后提出了基于相同犯罪特征频繁子图结构的挖掘犯罪规律算法GDMCR(Graph Data Mining Crime Rule),最后利用GDMCR算法得到的频繁子图关联知识分析犯罪规律及网络核心成员.实验证明了文中提出的基于图数据挖掘犯罪规律分析系统的有效性和实用性,并验证了GDMCR算法的有效性.%The data mining technologies applying to analyze the crime rule has become a hot spot in field of the public security information system, there is little work being done on analyzing the crime rule of criminal and terrorist groups. Compared with other data technology , graph can express richer semantic meaning. It is a new paradigm to apply based on graph data mining algorithm to analyze the crime rules. To mine crime rule and key members of a crime group, first proposed theory based on graph data mining, then proposed a frequent subgraph of same crime characteristics based algorithm called GDMCR (Graph Data Mining Crime Rule ), finally employed frequent subgraph analysis techniques to discover crime rule and key structure. The experimental results show the efficiency and usability of the crime rule analysis system based on graph data mining, and demonstrate that GDMCR is efficient.
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