Applying Ant Colony System in Data Mining under Multi－Dimensional Constraints

• Main Authors: Harry Shih, 施智文
• Other Authors: Ren-Jieh Kuo
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/01699257668873086343

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 92 === It is an important issue for association rules generation in the area of data mining. Frequent itemset discovery is the key factor in the implementation of association rule mining. Therefore, this study intends to consider the user’s assigned constraints in the mining process. Constraint-based mining enables users to concentrate on mining itemsets that are interesting to themselves, which improves the efficiency of mining tasks. In additional, in the real world, the users may prefer recording more than one attributes and setting multi-dimensional constraints. Thus, this study intends to solve the multi-dimensional constraints problem for association rules generation. Ant colony system (ACS) is one of the most recently applied meta-heuristics for combinatorial optimization problems. Using ant colony system to mine the large database could find the association rules effectively. If it can consider for multi-dimensional constraint, the association rules will be generated more effectively. Therefore, this study proposed a novel approach applying ant colony system for extracting the association rules from the database. In addition, the multi-dimensional constraints are put into account. The results using a real case, the National Health Insurance Research Database show that the proposed method is able to provide more condensed rules than Apriori method. The computational time is also reduced.