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中文摘要：
就常常在象电线转移数据库那样的大数据库数据增加而言，增长聚类算法在数据挖掘(DM ) 起一个越来越重要的作用。然而，很少传统的聚类算法不能仅仅处理范畴的数据，而且清楚地解释它的产量。基于动态聚类的想法，一个增长有构思力的聚类算法在这篇论文被建议。它介绍 SemanticCore 树(SCT ) 为检测的钱处理大量范畴的电线转移数据洗。另外，规则产生算法这里被介绍由知识的格式表示聚类的结果。当我们在金融数据采矿使用这个想法时，寻找洗数据的钱的字符的效率将被改进。
英文摘要：
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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