Partition based Single Scan Method for Mining Frequent Item Sets
U. Mohan Srinivas1, E. Srinivasa Reddy2
1U. Mohan Srinivas, Research Scholar, Department of Computer Science & Engineering, Acharya Nagarjuna University, College of Engineering, Guntur, India.
2E. Srinivasa Reddy, Department of Computer Science & Engineering, Acharya Nagarjuna University, College of Engineering, Guntur, India.
Manuscript received on July 21, 2019. | Revised Manuscript received on August 17, 2019. | Manuscript published on August 30, 2019. | PP: 4917-4922 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9237088619/2019©BEIESP | DOI: 10.35940/ijeat.F9237.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Frequent Itemset mining (FIM) concept and limitations are explored in this paper, for the purpose of extracting unknown hidden patterns as itemsets from the transactional database. Since candidate generation and support calculations are the major tasks in FIM, the major limitations of FIM are tackled, (i) huge possible frequent itemsets are generated as candidates at each pass (ii) Data base scan at each pass to calculate the support of the generated itemsets (iii) generated itemsets are highly sensitive to the minimum support threshold. SS-FIM a single scan algorithm is to deal with the above limitations. However, several unnecessary itemsets are being hashed in the buckets. To overcome the limitations, a partition based approach is proposed in this paper. The proposed approach, PSSFIM, takes single scan of the database to identify frequent itemsets. The unique feature of PSSFIM allow to generate size of candidate itemsets independent on the minimum support. It allows the candidates in hash that are possible for frequent, which intuitively reduces the cost in terms of verifying the support of generated candidates. It is compared with SS-FIM and Apriori with the standard datasets. The results show that the PSSFIM is good at the comparison of SS-FIM and Apriori.
Keywords: Apriori, Frequent Itemsets, Run time, Support.