Data Mining Based On Association Rules Algorithm And Its Application

Database technology has been widely popularized and used since 1980s. With the expansion of the database capacity, especially some new data source has been more and more popularized such as Data Warehouse and Web. The main problem that people face is no longer lacking of adequate information for using but how to use the data effectually in the ocean data. Meeting this challenge, data mining comes into being. Data Ming is the information processing technology, which is developing very fast in recent years. Using data mining, People can abstract information and knowledge from a great deal of data which is incomplete, noisy dark and random. The information and knowledge we get was ignored and had not been known before but potentially useful.Association rules mining is an important sub-branch of data mining, which mines interesting association or correlation relationships among a large set of data items. Association rules are considered interesting if they satisfy both a minimum support threshold and a minimum confidence threshold, Association rules mining has become a hot research topic in recent years, and it has been used widely in elective marketing, decision analysis and business management.In this paper, we first explained the concept of Data Mining and its application in detail, and then introduces two algorithms: Apriori and FP-tree which is based on association rules, and compares the capability of this two algorithm. In allusion to the disadvantage of low space utilization rate and slower execution time when using FP-tree mines the large datasets, this paper proposes a improvement project. At last apply this data mining method that based on association rules to the teaching evaluation, and analyzes the mining result, proposes instructive opinion.