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.
Highlights
The purpose of FIs(Frequent Itemsets) mining [1]-[3] is to determine the sets of item in database looking frequently
When every FIs cannot stay positively mined from dense data type, an another solution, to mine whole FCIs and frequent maximal itemsets (FMI) from dataset, apply deriving effective algorithm to derive the set of complete Frequent Itemsets with their supports from FMIs and FCIs
We propose an algorithm, named DFCI-Mine (Deriving Frequent Itemsets from closed Itemsets) and RFMI (Recover all FIs from maximal Itemsets) for the FIs deriving task
Summary
The purpose of FIs(Frequent Itemsets) mining [1]-[3] is to determine the sets of item in database looking frequently. Evolve deriving efficient algorithm is a significant works for both FCIs and FMIs. When every FIs cannot stay positively mined from dense data type, an another solution, to mine whole FCIs and FMIs from dataset, apply deriving effective algorithm to derive the set of complete Frequent Itemsets with their supports from FMIs and FCIs. A maximal frequent itemsets has no super subsets is called frequents. The proposed algorithm, named DCFI-Mine (Discovering Compact Frequent Itemset is Pattern Growth based), for effective derive FIs and with the support from FCIs. An ideas design of DCFI-Mine construct the FP-Tree first to preserve the data of FCIs, from FP-tree the FIs is generated using FP-Growth. RMFI (Recover all Frequent Itemsets from maximal Items) to recover entire FIs from set of maximal FIs. DCFI-Mine comparing the performance is considerably better than RFMI algorithms in term of execution time and memory usages
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