Visual Data Mining Methods To Achieve

In recent years, with the fast development of the computer hardware and software, especially the great advance in Internet techniques, the volume of the data which people have accumulated is now increasing very fast.The whole volume is so large that it is hard to find knowledge hidden in such a large data set. This is a problem being studied by many people nowadays. Data mining is one of the ways to solve this problem. In data mining, visualization plays an important role. Visualization in data mining lets us combine the virtue of human being's vision and domain knowledge with that of data mining. This combination makes the process of data mining intuitionistic and interactive, and thus gains more valuable and more understandable information.In this dissertation, the techniques of visual data mining are researched and implemented in a Web based distributed data mining system, which was called MinerOnWeb.A section in this dissertation will introduce the specific design and implementation of the data mining system MinerOnWeb. MinerOnWeb is a system which is designed to provide data mining service on line. It is constructed under the Struts framework according to J2EE criteria. It integrates a group of algorithms related to classification, cluster, and association mining. It is able to cope with data in several kinds of format. This dissertation mainly focus on research and analysis of 2D/3D visualization techniques of data mining, then the application of these technologies on MinerOnWeb system at the following three aspects:(1) Data Source Visualization Based on 3D Scatter Plot:This module obtain the data source,The 3D visualization technology provide more intuitive data analysis for pre-processing stage in data mining.Property after selection can be displayed in the 3D coordinates, and also can be rotated, placidly move, scale and locate point.(2) Association Rules Mining Visualization Based on 2D Table: The 2D table visualization technology have more advantages to display the results of association mining than the traditional methods.The intuitive 2D graphics combine with a norms table to display the association rules,it also provid property selection filters and interactive features, so that the mining results will be displayed intuitively.(3) Cluster Mining Visualization Based on SOM (Self-Organizing feature Map):Inder to understand the characteristics of automatic cluster,I design and implement the module of visual cluster mining.This module provide the function of cluster mining visualization using many visual technology like as histograms, scatter plots and two-dimensional tables and so on.