Research On A Model And Mining Algorithm Of Weighted Association Rules

With the rapid development of information industrialization, the capabilities of data productivity and collection increase rapidly. New requirements have been raised for new technology and automatic implement to transform magnanimity data to useful knowledge. Data Mining comes out for the demands. Data mining is the process of mining the latent, previous unknowned, potentially useful knowledge in abundant, uncompleted, noisy, fuzzy, random data.Data Mining have many research points. Mining association rules is one of the important hotspot. To sovle the different importance and unbalance of individual items in database, the paper will put the emphasis on mining the weighted association rules. The main research are as follows:1. This paper briefly introduces the development regarding the concept-based retrieval. Based on this introduction, this paper analyses the main problems and direction of study in the future.2. Research on a famous Apriori Algorithm for mining frequent itemsets for Booleam association rules and an improved FP-growth Algorithm for mining frequent itemsets without candidate generation3. The famous algorithm--- MINWAL(O) Alogrithm is thoroughly studied. Some problems in this algorithm and others are pointed out.4. We propose a new model of mining weighted association rules and algorithm of mining weighted frequent itemsets called MWFI, which design process and performance study are discussed at large. Though test the algorithm can mine informations which better show decision-maker's practical demands.