Research On Behavioral Analysis In Foreign Exchange Transaction Based On Data Mining

In foreign exchange transaction, some enterprises may have similar behavior because of their own feature or similar business. It is helpful to discover these business objects which have similar behavior that these nature feature are used to describe the behavior characteristic of business objects, and these business objects are grouped according to behavior standard. It can make the supervisory department more deeply recognize business objects, understand the common behavior features of similar objects, and know the real situation of business behavior. In conclusion, it is a good supplementary for the management and supervision of foreign exchange to discover and cluster these business objects which have similar behavior through those natural features.There are two phases to carry out behavioral analysis of foreign exchange transaction using data mining and combining practical application which are data preprocess and behavioral analysis. Data preprocess is the basis of behavioral analysis, and it is a process of preparing data including choosing data, selecting attributes, building data model and so on, then business behavior table is produced. Behavioral analysis consists of two steps. One is data transforming which finishes the transform between business behavior table and business object table , the other is to complete clustering business objects by Hypergraph- based Clustering Algorithm.It is the chief task to resolve attribute discretization before describing business behavior. In this paper we describe a unsupervised discretization algorithm by statistical analysis with slip window which bring a good result for continuous attribute of large value range and asymmetric distributing. Added to this, it is simple and easy to be achieved, and is usually completed through scanning statistical histogram once. Though the value range of the attribute is large, we can gain the reasonable subsections according to the distributing situation.In view of the conventional clustering algorithm which scale the similarity between objects through the distance metric and not get a good cluster purpose for multidimensional categorical data, a new hypergraph-based behavioral analysis method builds hypergraph model using association rules, describes the similarity metric through the confidence of association rules in every hyperedge and tightness among the hyperedges, and carry through hypergraph partition to cluster objects according to the feature of foreign exchange dealing. In this way, it is achieved that those business objects of similar behavior can be discovered. At the same time, it uses hyperedge to reduce the scale of data set in the process of clustering. In this way, it is applicable for clustering large data set...