Mining Closed Item sets u sing Partition Based Single Scan Algorithm
U. Mohan Srinivas1, E. Srinivasa Reddy2
1U. Mohan Srinivas, Research Scholar, CSE, ANUCET, Guntur, India.
2E. Srinivasa Reddy, Prof. and Dean, CSE, ANU, UCET, Guntur, India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 3885-3889 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1920058119/19©BEIESP | DOI: 10.35940/ijrte.A1920.078219
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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: Closed item sets are frequent itemsets that uniquely determines the exact frequency of frequent item sets. Closed Item sets reduces the massive output to a smaller magnitude without redundancy. In this paper, we present PSS-MCI, an efficient candidate generate based approach for mining all closed itemsets. It enumerates closed item sets using hash tree, candidate generation, super-set and sub-set checking. It uses partitioned based strategy to avoid unnecessary computation for the itemsets which are not useful. Using an efficient algorithm, it determines all closed item sets from a single scan over the database. However, several unnecessary item sets are being hashed in the buckets. To overcome the limitations, heuristics are enclosed with algorithm PSS-MCI. Empirical evaluation and results show that the PSS-MCI outperforms all candidate generate and other approaches. Further, PSS-MCI explores all closed item sets.
Index Terms: Data Mining, Frequent Itemsets, Closed Itemset, Minimum Support.
Scope of the Article: Data Mining