相联规则的粗熵挖掘方法及其在肇事逃逸侦破中的应用  

Mining Association Rule on Rough Entropy Basis in Detecting Escapes from Traffic Accidents

作　　者：赵海[1] 陈燕[2] 张德干[1] 张晓丹[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004 [2]大连海事大学管理系,辽宁大连116000

出　　处：《东北大学学报（自然科学版）》2004年第10期938-941,共4页Journal of Northeastern University(Natural Science)

基　　金：辽宁省自然科学基金资助项目(辽科发[2001]113号).

摘　　要：针对传统数据挖掘算法在处理包含不确定性因素的多源信息场景中存在的因掺杂额外的人为因素而导致误差的缺陷,提出了一种基于粗糙熵的相联规则的挖掘方法,并给出了该方法的评析途径·将研究的方法应用于公安系统的交通肇事逃逸案的侦破中,从历史数据中挖掘出了相联规则,为公安系统对交通肇事逃逸案的侦破提供了一种高效和实用的手段·应用范例验证了该方法的有效性·Traditional data mining algorithms have unavoidably errors arising from additional man-made uncertain factors in dealing with multi-source information. A new mining method called association rule is therefore proposed basis in view of rough set theory, with another method specially designed to assess it. Method from historical data of the crimes escaping from traffic accidents in a designed way, the association rules will provide an efficient and practical means for the police to detect the crimes escaping from traffic accidents. Some applications have exemplified the effectiveness of the method proposed.

关 键 词：相联规则 粗糙熵 数据挖掘 交通肇事 逃逸侦破 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]