Partition Based Single Scan Approach for Mining Maximal Itemsets
U. Mohan Srinivas1, E. Srinivasa Reddy2
1U. Mohan Srinivas, Research Scholar, Dept. of CSE, Acharya Nagarjuna University, Guntur, India.
2Dr. E. Srinivasa Reddy, Professor, Dept. of CSE, Acharya Nagarjuna University, Guntur, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 54-59 | Volume-8 Issue-8, June 2019 | Retrieval Number: G5810058719/19©BEIESP
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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: In this paper, Frequent Itemset mining (FIM) limitations and compact representation of FIM that is Maximal Itemset explored for extracting unknown redundant less frequent itemset from the transactional database. The candidate generation and support calculations are the major tasks in FIM. FIM challenges with the following limitations for low support threshold: (i) huge frequent itemset are generated as output (ii) difficulty in taking decision among the frequent itemset due to redundancy. (iii) To find the cumulative support of itemset, database scan is required for each length. The first issue can be resolved using maximal itemset that are frequent who doesn’t have any superset is called as Maximal Itemset Mining (MIM). Maximal itemset are useful in minimal key discovery kind of applications. Hence, we present a Single scan algorithm to address the above limitations. However, several unnecessary itemset are being hashed in the buckets. To overcome the limitations, a partition-based approach is proposed. Empirical evaluation and results visualize that the PSS-MIM outperforms all candidate generate and other approaches.
Keyword: Data mining, frequent itemsets, Maximal itemset.
Scope of the Article: Web-Based Learning: Innovation and Challenges