EIFDD: An efficient approach for erasable itemset mining of very dense datasets

Author information: Mr. Tuong Le, the researcher of Division of Data Science of Ton Duc Thang University (DDS).

Tittle: EIFDD: An efficient approach for erasable itemset mining of very dense datasets

Journal information: The paper was published in the ISI journal Applied Intelligence (its impact factor is 1.215 and H-index is 43).

Abstract: Erasable itemset mining, first proposed in 2009, is an interesting problem in supply chain optimization. The dPidset structure, a very effective structure for mining erasable itemsets, was introduced in 2014. The dPidset structure outperforms previous structures such as PID_List and NC_Set. Algorithms based on dPidset can effectively mine erasable itemsets. However, for very dense datasets, the mining time and memory usage are large. Therefore, this paper proposes an effective approach that uses the subsume concept for mining erasable itemsets for very dense datasets. The subsume concept is used to help early determine the information of a large number of erasable itemsets without the usual computational cost. Then, the erasable itemsets for very dense datasets (EIFDD) algorithm, which uses the subsume concept and the dPidset structure for the erasable itemset mining of very dense datasets, is proposed. An illustrative example is given to demonstrate the proposed algorithm. Finally, an experiment is conducted to show the effectiveness of EIFDD.

 

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