A Study on Fuzzy Coherent Data-Mining Techniques

• Main Authors: Ai-Fang Li, 李艾芳
• Other Authors: Chun-Hao
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/14087444157136489011

碩士 === 淡江大學 === 資訊工程學系碩士班 === 100 === In real-world applications, transactions usually contain quantitative values. Many fuzzy data mining approaches have been proposed for finding fuzzy association rules from the give quantitative transactions. In addition, since each item has its own utility, utility itemset mining has thus become an interesting field in recent years. However, the common problems of those approaches are that: first, an appropriate minimum support is not easy to be set; second, the derived rules usually expose common-sense knowledge which may not interesting in business point of view. In this thesis, we thus propose two algorithms, called Fuzzy Coherent Rules (FCR) mining and High Coherent Utility Fuzzy Itemsets (HCUFI) mining, to overcome the mentioned problems with the properties of propositional logic. The first algorithm first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are further collected to generate candidate fuzzy coherent rules. Finally, contingency tables for every candidate fuzzy coherent rules are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. If yes, it is then a fuzzy coherent rule. In second algorithm, due to each item has its utility, we first propose a domain-driven fuzzy data-mining framework. According to the framework, we further propose a high coherent utility fuzzy itemsets mining algorithm for increasing patterns’ business merits. It first transforms quantitative transactions into fuzzy sets. Then, utility of each fuzzy itemsets is then calculated according to the given external utility table. If the value is large than or equals to the minimum utility ratio, it is considered as high utility fuzzy itemset (HUFI). Finally, contingency tables are calculated and used for checking those HUFIs satisfy specific four criteria or not. If yes, it is a High Coherent Utility Fuzzy Itemsets (HCUFI). Experiments on the foodmart dataset have also been made to show the efficiency of these two proposed approaches. The advantage of first algorithm is that it can derive business interestingness rules with propositional logic without setting minimum support. And, the advantage of second algorithm is that it can derive more actionable knowledge pattern with business interestingness based on the domain-driven fuzzy data-mining framework.