A Modified Algorithm for Generating Single Dimensional Fuzzy Itemset Mining
D. Ashok Kumar, R. Prabamanieswari, Govindammal Aditanar · 2011
Mining frequent itemsets from transaction database is a fundamental task for Association Rules. Aprior i influential algorithm for mining frequent itemsets using Boolean values. There are different motivatio ns for a fuzzy approach Association Rule Mining. An Algorithm for Generating Single Fuzzy Association Rule Mining is based on human in as the larger number of items purchased in a transa ction means that the degree of association among th e items in the transact may be lowered. The proposed approach modifies the above said Fuzzy Association Rule Mining algorithm and compares Apriori and the mentioned Fuzzy Association Rule Mining algorithm. The proposed approach calculates th e support value based on fuzzy t-norm namely intersection and finds the subsets of a frequent itemset partially. Therefore, it reduces the complexion of finding each subset of a frequent itemset.