Improved Frequent Item-Sets Mining in Pharmacovigilance
Kamatchi Sankar1, Latha Parthiban2

1Kamatchi Sankar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, (Tamil Nadu), India.
2Latha Parthiban, Department of Computer Science, Pondicherry University CC, Puducherry (Tamil Nadu), India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 13 August 2019 | Manuscript Published on 27 August 2019 | PP: 288-291 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10540782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1054.0782S419
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Abstract: Mining frequent item-sets is an important concept that deals with fundamental and initial task of data mining. Apriori is the most popular and frequently used algorithm for finding frequent item-sets which is preferred over other algorithms like FP-growth due to its simplicity. For improving the time efficiency of Apriori algorithms, Jiemin Zheng introduced Bit-Apriori algorithm with the enhancement of support count and special equal support pruning with respect to Apriori algorithm. In this paper, a novel Bit-Apriori algorithm, that deletes infrequent items during trie2 and subsequent tries are proposed which can be used in pharmacovigilance to identify the adverse event.
Keywords: Pharmacovigilance, Data Mining, Adverse Events.
Scope of the Article: Data Mining