Mining Algorithm Research For Association Rules Base On Interest Measure

Data mining is the knowledge discovery technique oriented to a great deal of data. Researching efficient algorithm is one of the important contents in study of data mining. Association rule is one of the important models of data mining, and has the most significant application value. The core of this dissertation is how to improve the validity and scalability of mining algorithm of Boolean association rules.The Apriori algorithm is the method of finding Boolean association rules, but has the disadvantage in the complexity of space and time. Therefore, this thesis introduces a new frequent-pattern (FP) growth algorithm that does not need to produce the candidate item sets. This algorithm compresses information in database to the FP-tree, then produces frequent pattern by joining suffix with prefix, consequently avoids scanning the database many times, and lowers the time expense.When there are a great many of items and transactions in the database, frequent-pattern growth algorithm needs more additional computer memory, which may cause the lack of memory. Therefore, this paper brings forward frequent-pattern growth algorithm based on maximum clique that resolves problem of memory insufficiency by dividing item set into several subsets, then computing frequent-pattern for each subset. In this paper, a new algorithm is given to find fraquent 2-itemset by adjacency matrix with less times scanning the database.How to select the interested and valuable rules from a large number of association modes is one of the important contents in study of mining algorithm. There is limitation in model based on support and confidence measure, thus interest measure model based on effect is given in this dissertation, which is used to prune the no-interest rules in order to discover the real interest rules mode.