Research On The Techniques Of Data Mining And Association

With the abroad application of database technology, every walk of life all accumulated many useful data, the inner relation of which maybe is valuable acknowledge, and it is urgent affairs that how to discover and pick-up the data, and it is necessity of data mining. Data mining refers to picking out patterns and characteristics in which users are interesting and other universal data character implicated in data.This thesis studies on the theories, techniques and the applications of DM, and the definition and characteristics of SDM are set forth, and a structure of spatial data mining system including data source, miner and user interface is put forward. The essential processes of SDM are studied and eight types of rules resulting in mining are discussed. There are several kinds of spatial data mining approaches researched in this thesis.Association rule mining is an important task of data miming. To discover a high-performance, high-speed classification is one of key problems for data mining.This thesis emphasizes on several techniques of association rule mining. At first, the background knowledge of data mining and association rule mining are introduced briefly. After that, some kinds of common used association rule mining algorithms are discussed respectively; among them certain typical algorithms are analyzed and compared thoroughly.Furthermore, the results visualization techniques of association rule mining are studied. The followed content is privacy preserving techniques. this thesis presents an improved algorithm based on Apriori algorithm and mends it for curricular association rule mining, and attain the anticipative experimental outcome .In the end, this thesis makes the conclusion and prospects of data mining research.