Mining Algorithm Based On Association Rules Of Logic And Computing

Data mining technology is an effective approach to resolve the problem of abundant data and scanty information. It currently is the research frontier within the information science field. The related researches and applications have greatly improved the ability for decision supporting. It has been deemed to a field that has broad prospect of application in database research. Database mining is motivated by the decision support problem faced by most large retail organizations. Progress in bar-code technology has made it possible for retail organizations to collect and store massive amounts of sales data, referred to as the basket data. We can get some information useful for sale or produce procedure through mining in the data while that information usually reflected by a certain pattern. This paper describes the conception, function and patterns of data mining. In many data mining algorithms, mining association rule is an important matter in data mining, in which process that mining frequent itemset is a key problem in mining association rule.There are many algorithms of association rules mining for searching frequent itemset, Apriori is one of the most influences algorithm. Many researchers also give a lot of improved ideas. Many of the previous algorithms mine frequent itemset by producing candidate itemset firstly, then pruning. But the cost of producing candidate itemset is very high, especially when there exist long patterns. This paper studies mostly the problem of mining frequent pattern based on using logic and Operation.Firstly, we study the definition and construction of the frequent itemset support matrix and improved algorithms and analyze the feasibility and completeness of the mining process. Then, we propose the algorithm for mining frequent pattern in which based on the logic and operation--FIMA (Frequent Itemset Mining Algorithm). This algorithm need not produce candidate itemset, also only needs scanning database one times. This algorithm use matrix to save frequent itemset, this more easy to program than use graph to save frequent itemset, and saved more memory. At last, our experimental result shows that the algorithm FIMA is more effectively than the algorithm DLG based on graph for mining frequent patterns.Secondly, we study the problem of mining valid and Non-Redundant association rules. The traditional algorithm mining association rules, or slowly produces...