Research Adn Implement On Data Mining Technology Of Community Service-Oriented

As one of the important contents in data mining, association rule mining aims to discover the interesting connection or the correlation midst a set of objects in a database. Association rule mining has become a hot research topic in recent years, and it has been used widely in selective marketing, decision analysis and business management.Rules are thought as invariable in the former mining arithmetic, that to say, the rule are just static rules. In fact, data characteristics and rules may take huge changes during the process of time, therefore maintaining the availability of association rules mining is essential especially. In this thesis, some classical algorithms for mining association rules have been systematically studied and comprehensive summarized. On the basic of previous research, the novel algorithm for association mining rules and incremental updating of association rules are proposed, and implemented on the data mining system.First, this paper introduced the basic tasks of data mining and technology. Focus on the three classics association rule mining algorithms:Apriori, close algorithm and FP-Growth algorithm. It takes a comprehensive analysis on Apriori improving and the advantages and disadvantages of FP-Growth algorithm.Second, this paper describes the core ideological, architecture, basic steps and pseudo-code of association rule algorithm based on dynamic data. Then describes the characteristics and performance evaluation standard of this algorithm.At last, based on the analysis and summarize of association rule mining algorithms, implement the design and package of this algorithm on the data mining service software system, then test the performance of this algorithm. Form the result of test, we know this algorithm has more efficient. The corresponding evaluation take full account of the time characteristics of dynamic data, is suitable for the theme-oriented dynamics data mining and has good scalability.