Now a day, Association rules mining algorithms used to increased turnover of any product based company. Therefore, many algorithms were proposed to determine frequent itemsets. This paper also proposes a novel algorithm, which is resulting from merging two existing algorithms (i. e. Partition with apriori and transaction reduction algorithm) to derived frequent item sets from large database. The experiments are conducted to find out frequent item sets on proposed algorithm and existing algorithms by applying different minimum support on different size of database. It shows that designed algorithm (pafi with apriori algorithm) takes very much less time as well as it gives better performance when there is a large dataset. Whereas with increase in dataset, Apriori and Transaction reduction algorithm gives poor performance as compared to PAFI with apriori and proposed algorithm. The implemented algorithm shows the better result in terms of time complexity. It also handle large database with efficiently than existing algorithms.

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PAFI clustering Transaction reduction