Wavelet Analysis Applied Research, Scientific Data Mining

With accelerating pace of science development, the demands on capability of large-scale information analysis are increasing. Data Mining is the process through which we can extract hidden but useful knowledge from half-backed, noising, fuzzy and random data sets. In order to obtain satisfying results, it is necessary for the database to be preprocessed before the actual mining. Preprocessing converts the original dirty data into normalized data. Data denoise and compress are the core of the whole process. How to remove noise and compress data is highlighted in both technology and theory research. In recent years, Wavelet theory has shown potential in data denoise and compress. Comprehensive researches and tests reveal that applying Wavelet method on scientific data to denoise can improve the efficiency of pattern recognition.Scientific data has the characteristic of high dimensional, incomplete, noising, and so on. Our purpose is to develop an open, easily maintainable, extensible and user-friendly Scientific Data Mining System(SDMS) which can extract useful knowledge from scientific data and analyze the simulation result of scientific computing. The system is hosted on DBMS and Windows platform. Currently a prototype system with functions of data denoise, compress, attribute reduction, discretion, classifying and clustering has been completed. Test results show that the system has basically achieved the designing requirement.This thesis mainly discusses topics on solving data denoise and compress problems by applying wavelet analysis methods in the field of scientific data mining. First, basic concepts of Data Mining are introduced; then the theory of wavelet analysis and its application on data denoise and compress is provided; in the 3rd chapter, some common data denoiseand compress algorithms based on wavelet analysis theory are explained, and new algorithm is proposed; finally, functionality of the data mining system is presented and the performance of wavelet denoise and compress in the system are shown by some experiments.