Fuzzy QMD algorithm for mining fuzzy association rules
Chien-Hua Wang, Wei-Hsuan Lee, Chia-Hsuan Yeh, Chin-Tzong Pang · Proceedings of the 3rd International Conference on Communication and Information Processing · 2017
Association rules mining is to find associations efficiently among the different items of a transaction database. In order to help decision-makers conduct sound and timely solutions, we apply fuzzy partition method and combine QMD (Quick Modulized Decomposition) to propose a novel fuzzy data mining method. The proposed method is mainly generated fuzzy itemsets by MAP modulized, and uses fuzzy minimal fuzzy support and minimum fuzzy confidence to generate fuzzy association rules. The method only needs to scan whole transaction database once and uses this modulized method to increase the performance of mining process. Furthermore, in fuzzy partition, the linguistic values of each fuzzy grid were obtained easily and the decision maker makes correct business decisions for marketing strategies.