International Journal of Engineering and Technology

Association Rule Mining (ARM) algorithm motivates on mining of the Positive Association Rules (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 and treat each item with same significance. But, the significance of each item may differ from each other. This paper proposes a Fuzzy-based Positive and Negative Weighted Association Rule (F-PNWAR) mining algorithm for the market-based data analysis. The itemsets are ranked and weight is assigned to the itemsets based on the rank. The positive and negative weighted itemsets are extracted and rule is generated. 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 K-means algorithms. The comparative analysis shows that the proposed F-PNWAR algorithm achieves maximum frequency item rate, association rule rate, accuracy and minimum execution time than the existing algorithms.