Study Of Data Mining Algorithms Based On Rough Set And Clustering And Application In Anti-Money Laundering

The task of Data Mining is to find great deal of knowledge that have not yet been found ,particularly the relations and treads hidden in the data. The characteristics and functions of Data mining meet the demand of anti-Laundering Monitoring and Analysis system. Many algorithms in Data Mining field have a good prospect in the anti-laundering field. The research of anti-laundering monitoring and analysis of data by data mining technology is a hot spot. Therefore, the studies for the key algorithms that are suitable for data features of financial transactions data and should be used in the anti-laundering system is of great value in the theoretical study and practical application.Rough set theory is a new mathematical tool dealing with vagueness and uncertainty, has found its applications in many areas such as AI, KDD, pattern recognition and classification and fault diagnostication. A rough set based mining algorithm is proposed to generate decision model in the anti-money laundering system. The generated model is employed to find suspicious transactions. The algorithm first reduces the attributes by constructing discernibility matrix. Then rules are found from the training data set. The algorithm can be helpful when the dependency of decision and classification attribute is vague and the data set is not full.In view of the conventional clustering algorithm which scale the similarity between objects through the distance metric and not get a good cluster result for high dimensional data, a new hypergraph-based is proposed, which formulates the data clustering problem in a high dimensional space as a hypergraph partition optimal problem. It is applied to the clustering of high-dimensional data that high-dimensional space is transformed into the hypergraph, the relationship between points is describe by the weight of the super edge. Segmentation on the hypergraph is just a clustering process during which put the points contained in the hypergraph with larger weight into one class, while make the sum of the weight of the segmented super edge smallest.It does not require dimensionality reduction,people can filter out noise data from the clusters very effectively and control the quality of the cluseters.In order to explain the semantics of clusters generated by clustering algorithm, the semantic centers is proposed to describe the outputting result by concepts, which makes algorithm represent the characters of clusters better. Comparing to traditional clustering algorithms, the conceptive clustering can accommodate itself to category data much better. The explanative rule based incremental conceptive clustering algorithm is proposed can get the semantics of the clustering results, and discovery deeper hidden information by generating rules with calculating the assurance factor and subsumption factor.Based on the study aboved, according to the specific anti-money-laundering situations of China and learning from the results and experience of the building of anti-money-laundering system in the United States, Canada, Australia, and other countries, study the anti-money laundering information system which is fit for the situation of China. Based on the analysis of the anti-money-laundering system in the background, and the base of information ,set the goal of building the system ,raise the anti-money-laundering system of the overall framework, which includes information supporting verification platform ,Analysis platform and Anti-Money Laundering Data Mining Platform .Based on the above theory and research results, combined with the exchange of data integration, data warehouse and OLAP technology, develop and imply an anti-money laundering information system that has been successfully applied in the practical application of the Anti-Money Laundering by the State Administration of Foreign Exchange, and across the country. The system is the first professional, intelligent anti-money-laundering information management system developed by China, achieve and strengthen anti-money-laundering data analysis and processing, improve the efficiency and quality of the work of anti-money-laundering, and get satisfactory results . The project won the bank of second prize in People's Bank of China Technology Development in 2006.