Deriving Frequent Itemsets from Lossless Condensed Representation
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 January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 209-214 | Volume-9 Issue-3, February 2020. | Retrieval Number:  B4438129219/2020©BEIESP | DOI: 10.35940/ijeat.B4438.029320
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Abstract: In data mining, major research topic is frequent itemset mining (FIM). Frequent Itemsets (FIs) usually generating a large amount of Itemsets from database it causing from high memory and long execution time usage. Frequent Closed Itemsets(FCI) and Frequent Maximal Itemsets(FMI) are a reduced lossless representation of frequent itemsets. The FCI allows to decreasing the memory usage and execution time while comparing to FMIs. The whole data of frequent Itemsets(FIs) may be derived from FCIs and FMIs with correct methods. While various study has presented several efficient approach for FCIs and FMIs mining. In sight of this, that we proposed an algorithm called DCFI-Mine for capably derive FIs from Closed FIs and RFMI algorithm derive FMIs to FIs. The advantages of DCFI-Mine algorithm has two features: First, efficiency, different existing algorithm that tends to develop an enormous quantity of Itemsets all through process, DCFI-Mine process the Itemsets straight without candidate generation. But in proposed RFMI multiple scan occurs due to search of item support so efficiency is less than proposed algorithm DCFI-Mine. Second, in terms of losslessness DCFI-Mine and RFMI can discover complete frequent itemset without lapse. Experimental result shows That DCFI-Mine is best deriving FIs in term of memory usage and executions time.
Keywords: Deriving algorithm, Frequent itemset mining, maximal itemset, closed, itemset mining, Lossless condensed representation.