| The Research of an Incremental Conceptive Clustering Algorithm and Its Application in Detecting Money Laundering |
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| 作者姓名： | CHEN Yunkai LU Zhengding LI Ruixuan LI Yuhua SUN Xiaolin |
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| 作者单位： | College of Computer Science and Technology, HuazhongUniversity of Science and Technology, Wuhan 430074,Hubei, China |
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| 基金项目： | 国家自然科学基金;湖北省自然科学基金 |
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| 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.
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| 关 键 词： | 增加概念聚类 SCT DM 数据挖掘 |
| 文章编号： | 1007-1202（2006）05-1076-05 |
| 收稿时间： | 2006-03-20 |
The research of an incremental conceptive clustering algorithm and its application in detecting money laundering |
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| CHEN Yunkai LU Zhengding LI Ruixuan LI Yuhua SUN Xiaolin.The Research of an Incremental Conceptive Clustering Algorithm and Its Application in Detecting Money Laundering[J].Wuhan University Journal of Natural Sciences,2006,11(5):1076-1080. |
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| Authors: | Chen Yunkai Lu Zhengding Li Ruixuan Li Yuhua Sun Xiaolin |
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| Institution: | (1) College of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China |
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| Abstract: | 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. |
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| Keywords: | categorical DM incremental conceptive clustering SCT money laundering |
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