Intelligent Integration Of Data Mining Method And Its Application

With prompt development of computer technology and wild application of data base and its management, the amount of data being collected in databases today far exceeds our ability to analyze data and understand its meaning without the use of automated analysis techniques. There is much important information in the astronomical data. The data was expected to be more deeply analyzed to apply the data and provide decision making supports. Data mining is a kind of new technology that is evolving to provide automated analysis solutions. In modern times, data mining becomes one of hot topic in information technology field.Data mining is an information extraction activity whose goal is to discover hidden facts contained in databases. Clustering, principal component analysis (PCA) and trend analysis are three important parts in data mining process.Clustering analysis is an important approach of data mining. This dissertation firstly went deep into discussing some traditional clustering algorithms, then proposed a new integrated clustering analysis algorithm, a clustering analysis algorithm based on DDW and ANN and a fusion clustering analysis algorithm based on DDW. Furthermore, this dissertation compared and analyzed these methods, and applied these algorithms for data mining. The simulation demonstrated the effectiveness and practicability of these algorithms proposed.The classical PCA was one of the most important methods in statistic method. PAC could be used to compress data from multi-dimensions data. But when the data is Rotundity Scatter, the representative Principal Components could not be selected. This dissertation proposed a relative principal component analysis (RPCA) algorithm. The Relative Principal Components selected by RPCA are more representative, and their significance of geometry is more notable. The successful fault detection demonstrated the effectiveness of RPCA method.