| Summary: | 碩士 === 國立臺北科技大學 === 工業工程與管理系所 === 94 === With the development of information technology (IT), how to find useful information existed in vast data has become an important issue. The most broadly discussed technique is Data-mining, which has been successfully applied to many fields as analytic tool. Data mining extracts implicit, previously unknown, and potentially useful information from data. Association rule is one of the most important and useful technologies in data mining methods. Association rule summarizes meaningful relations among items, and this technology is typically applied to basket analysis in supermarkets.
Most of previous researches focus on improving computational efficiency. However, there are also some other researches which emphasize on how to decide the threshold values of support and confidence parameters. The reason is that deciding suitable threshold values is critical to the quality of association rule mining. In this study, we propose a new algorithm for association rule mining in order to improve the whole efficiency and determine suitable threshold values. At first, transaction data are transformed into binary formats and then we apply Particle Swarm Optimization (PSO) algorithm to search the optimum fitness value of particle and find its corresponding support and confidence as minimum threshold. The proposed method is verified by applying FoodMart2000 database of Microsoft SQL Server 2000 and compared with genetic algorithm in efficiency. According to the results, it is found that particle swarm optimization algorithms can really suggest suitable threshold values and obtain the quality rules. We also apply real-world stock market database in order to mine association rule among investment behavior and stock category purchasing. The computational result is also very promising.
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