Frequent Itemset Mining in a Unique Scan using Transaction Database
A. Subashini1, M. Karthikeyan2

1A.Subashini*, Assistant Professor in the Department of Computer Application, Government Arts College, C.Mutlur, Chidambaram, Tamil Nadu, India.
2M.Karthikeyan, Assistant Professor in the Division of Computer and Information Science, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 24, 2020. | Manuscript published on March 10, 2020. | PP: 612-617 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2477039520/2020©BEIESP | DOI: 10.35940/ijitee.E2477.039520
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Abstract: In recent year, frequent Itemset Mining (FIM) has occurred as a vital role in data mining tasks. The search of FIM in a transactions data is discovered in this paper, pull out hidden pattern from transactions data. The main two limitation of the Apriori algorithm are undertaken, first, its scans the complete Databases at every passes to compute the supports of every itemset produced and secondly, the user defined responsive to variation of min_sup (minimum supports) thresholds. In this paper, proposed methodology called frequent Itemset Mining in unique Scan (FIMUS), needs a scan only one time of transaction databases to extract frequent itemsets. The generation of a static numbers of candidate Itemset is an exclusive feature, individually from the threshold of min_sup, which reduces the execution time for huge database. The proposed algorithm FIMUS is compared with Apriori algorithm using benchmark database for a dense databases. The experimental result confirms the scalability of FIMUS. 
Keywords: Frequent Itemset Mining, Itemset Mining, Unique Scan, Apriori, Min_Sup.
Scope of the Article: Advance Concept of Networking and Database