Utilizing Fuzzy Theory and FP-Growth to Mine Fuzzy Association Rules for Hierarchy Structure

• Main Authors: Yi-Chen Liang, 梁怡貞
• Other Authors: Chin-Tzong Pang
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/46657788396001083930

碩士 === 元智大學 === 資訊管理學系 === 101 === Regarding the data mining technique, the application of association rule is used most widely especially for retail business, whose main purpose is to find out the association between items and extracts valuable information with association for further analysis and prediction. In many applications, introducing different hierarchy Structure to discover the association rules is very useful. The association rule used in a lower hierarchy concept could present more information than a higher hierarchy concept. For a decision problem, it must consider the cognition uncertainty generated by the user’s cognition and subjective judgment. Hence, this research based on the hierarchy concept structure and used the table structure fuzzy FP-Growth for mining. In the structure, each node is viewed as a linguistic variable. The multiple-level fuzzy association rules which are of cross hierarchy and can be interpreted by nature language. At last, the result derived from the experiment was analyzed and the proposed methodology was explored regarding the computational time and the quantity variation of the mined rules according to different size of databases set and the related parameter settings. The proposed method was also compared and analyzed with Hong et al.'s method and Hu's method and the experiment result indicated that the proposed method could effectively shorten the computational time of mining as well as having good performance on the efficiency.