F-PNWAR: Fuzzy-based Positive and Negative Weighted Association Rule Mining Algorithm

摘要

Association Rule Mining (ARM) algorithm motivates on mining of the Positive AssociationRules (PARs). In recent times, the researchers focused on mining the Negative Association Rules (NARs)by finding the interesting infrequent itemsets. Existing ARM algorithms discovers only the PARs andtreat each item with same significance. But, the significance of each item may differ from each other. Thispaper proposes a Fuzzy-based Positive and Negative Weighted Association Rule (F-PNWAR) miningalgorithm for the market-based data analysis. The itemsets are ranked and weight is assigned to theitemsets based on the rank. The positive and negative weighted itemsets are extracted and rule isgenerated. The proposed F-PNWAR algorithm is compared with the existing weighted ARM (WARM),Fuzzy WARM (FWARM), Enhanced FWARM (E-FWARM), traditional K-means and Adaptive Kmeansalgorithms. The comparative analysis shows that the proposed F-PNWAR algorithm achievesmaximum frequency item rate, association rule rate, accuracy and minimum execution time than theexisting algorithms.