Evolutionary Neural Network Clustering Technique In Data Mining Applications

Data mining technology is an effective approach to resolve the problem of abundant data and scanty information. It currently is the research frontier within the information science field. The related researches and applications have greatly improved the ability for decision supporting. It has been deemed to a field that has broad prospect of application in database research. As an important part of DM, Clustering Analysis consists Partitioning Method, Density-Based Method, Grid-Based Method, and Model-Based Method. This paper discusses the developing status of Clustering technology recently, compares the characteristics of all these arithmetic and points out the advantages and disadvantages of them. Specially, this paper makes deeper research on SOM-Based Clustering Technology.SOM-Based Clustering Technology is an integration of neural technology and DM. It has a strong connection with the process of brain. But this method has many shortcomings in respect of efficiency, optimization and the parameter, which determine the structure of the network.Evolutionary Neural Network is an active research field recently these days. Its idea of evolutionary includes three levels: the evolutionary of connection weights, the evolutionary of net structure and the evolutionary of study rules. Evolutionary Neural Network provides a new idea for the design of Artificial Neural Network, the improvement of performance of Neural System and the integration of Networks.Based on the traditional SOM-based Clustering technology and enlighten by the Evolutionary Theory, this paper introduces a new clustering methods based on Evolutionary SOM. It evolves the weights of the cells and optimizes the structure of the network by "splitting" the cells.This paper describes the arithmetic in detail, discusses the criterion of quality of Clustering and controls the growth of the network using it. The experimentationshows it overcomes the shortcomings of SOM Method listed above. And because of optimizing of the network structure, ESOM is more efficiency and accurate than genetic-based Method.Lastly, this paper introduces a system framework of a Decision Support System, and discusses the operation of the method in this system.