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The Research and Application of Weighted Association Rules Mining Algorithm

Author: ZhangQiuFeng
Tutor: ZhangGuiZuo
School: Tianjin Normal University
Course: Educational Technology
Keywords: Data Mining Association rules Apriori algorithm Weighted Association Rules Personalized Recommendation
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 82
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Abstract


Data mining knowledge and rules can be extracted from a large database or data warehouse implicit, previously unknown potential value to the decision-making. Association rule mining is a very important research direction in data mining, used to discover the relationship between the entries in the database. Whether to generate frequent itemsets angle, association rule mining algorithm is divided into two categories: generate frequent itemsets algorithm and does not generate frequent itemsets algorithm, Apriori algorithm and FP-growth algorithm classic representative, but both are not taken into consideration the importance of the item in the database. This article focuses on the weighted association rule algorithm, the main work and innovation are the following: First, the theoretical knowledge of data mining and association rules, focus on the basic idea of ??the Apriori algorithm analysis and improvement briefly its application in the field of web data mining. Second, because they do not consider the importance of database project will generate uninteresting rules, and the introduction of the project for the association rules Weighted thinking, in-depth study of several weighted association rule mining algorithms and models. Analyze the advantages and disadvantages of the existing weighted association rules model and algorithm, elaborated on the idea of ??improved algorithm; proposed weighted association rules based on matrix improved algorithm. Through a scan, a relational database to store the converted 0-1 matrix form, reducing the memory space occupied; connected computing frequent (k-1) - set the pre-pruning, and improved pruning strategy ; algorithm does not produce a candidate set, but directly generate frequent itemsets; superset may be caused due to the introduction of the value of the right to non-frequent itemsets frequently, so be considered separately weighted frequent 2 - itemsets generated, do not miss weighted The frequent set; generate association rules, and introduced a degree of interest constraint. The pseudo-code and flowchart of the algorithm is given through examples and experiments illustrate the feasibility and advantages of the algorithm. Finally, personalized recommendation process, will improve the field of personalized recommendation algorithm is applied to the knowledge point. Personalized Recommendation contains the offline part and online in this section, the main advantage of the algorithm in the offline part of the savings Offline weighted association rules. Through the simulation experiment to prove the feasibility of the algorithm.

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