The Research of an Incremental Conceptive Clustering Algorithm and Its Application in Detecting Money Laundering  

作　　者：CHEN Yunkai LU Zhengding LI Ruixuan LI Yuhua SUN Xiaolin 

机构地区：[1]College of Computer Science and Technology, HuazhongUniversity of Science and Technology, Wuhan 430074,Hubei, China

出　　处：《Wuhan University Journal of Natural Sciences》2006年第5期1076-1080,共5页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foun-dation of China (60403027) ;the Natural Science Foundation of HubeiProvince (2005ABA258);the Opening Foundation of State KeyLaboratory of Software Engineering (SKLSE05-07)

摘　　要：Considering the constantly increasing of data in large databases such as wire transfer database, incremental clustering algorithms play a more and more important role in Data Mining （DM）. However, Few of the traditional clustering algorithms can not only handle the categorical data, but also explain its output clearly. Based on the idea of dynamic clustering, an incremental conceptive clustering algorithm is proposed in this paper. Which introduces the Semantic Core Tree （SCT） to deal with large volume of categorical wire transfer data for the detecting money laundering. In addition, the rule generation algorithm is presented here to express the clustering result by the format of knowledge. When we apply this idea in financial data mining, the efficiency of searching the characters of money laundering data will be improved.Considering the constantly increasing of data in large databases such as wire transfer database, incremental clustering algorithms play a more and more important role in Data Mining （DM）. However, Few of the traditional clustering algorithms can not only handle the categorical data, but also explain its output clearly. Based on the idea of dynamic clustering, an incremental conceptive clustering algorithm is proposed in this paper. Which introduces the Semantic Core Tree （SCT） to deal with large volume of categorical wire transfer data for the detecting money laundering. In addition, the rule generation algorithm is presented here to express the clustering result by the format of knowledge. When we apply this idea in financial data mining, the efficiency of searching the characters of money laundering data will be improved.

关 键 词：CATEGORICAL DM incremental conceptive clustering SCT money laundering 

分 类 号：TP311.135.4[自动化与计算机技术—计算机软件与理论]