The Research Of Clustering Based On Gravity And Its Application

Data mining is the important application of technology in recent years, data clustering is the important branch of data mining. This kind of technology is to separate those not classified samples to some groups by its similarity, making the similarity in one group is bigger and in different groups is smaller, thus, finding the some internal properties and pattern. However, the structure and distribution of some data sets show high complexity, data mining will bring a lot of problems need to be solved for the clustering. Therefore, There is still great space to further study for suan an approach.Hierarchical clustering method is a common clustering algorithm, which create a hierarchy by decomposing given data object sets. Based on the direction of decomposition, Hierarchical clustering can be divided into two methods:bottom-up (condensed) method and top-down (split) method.Cover algorithm is constructive learning algorithm, by finding a group of cover, making the same type of samples belong to the same coverage, different types of samples belong to different coverage. Refer to constructive ideas of Cover algorithm, cover clustering algorithm try to find a group of cover, make the distance smaller in the same cover and the distance larger between different covers.From the initial Big Bang, all matter in the universe is in a chaotic state. As the role of gravity, making the matter in the universe attract each other, and then fuse to form the galaxies, starts, planets and other celestial bodies. This process is very similar to the process of clustering, according to some kinds of cluster computing, the chaos data ultimately become a clear structure of the clustering results. It is this similarity, we improve similarity measurement method by bring gravity into the clustering algorithm, from simple distance as the similarity to the cluster size as one parameter of similarity. This paper research the Hierarchical Clustering Algorithm (HC) and the Covering Clustering Algorithm (CCA), in both algorithms, using gravity in stead of distance as the similarity, propose Hierarchical Clustering Algorithm based on Gravity (HCBG) and Covering Clustering based on Gravity (CCBG). The results show that the gravity as similarity can improve clustering quality.Customer Relationship Management (CRM) is a management philosophy, also is a management software and technology. CRM involves the best business practices, data mining, data warehouse, one to one marketing, sales automation and other information technology. CRM provides a business automation solution that can help company to sale productions and help manager to make decision. Customer segmentation is an important research direction of CRM, by effective classification of customers and targeted marketing strategies, to achieve sales profit maximization. In the customer segmentation, the two most important steps are data mining and decision support, data mining try to find out clustering customers that have the similar behavior; decision support by Bayesian classification, decision tree and other methods, according to customers'personal data to predict his behavior. In this paper, use the HCBG which proposed in the third chapter to do data mining, meanwhile, to predict customer behavior by Bayesian classification methods.