A new enriched exploration of modified algorithm for generating single dimensional fuzzy itemsets

Mining frequent itemsets from transactional database is a fundamental task for association rules. Apriori is an influential classic algorithm for mining frequent itemset. But Apriori is a very slow and inefficient algorithm for very large datasets. A modified algorithm for generating single dimensional fuzzy itemset mining find support count based on fuzzy t-norms namely intersection for finding frequent itemset to reduces the processing time. The proposed method modifies the above mentioned algorithm for fast and efficient performance on large datasets. It adopts a new count-based transaction reduction and support count method for generating frequent fuzzy item set. So, it can further reduce time when compared to Apriori and above said algorithm.