NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets

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Huy Quang Pham, Duc Tran, Ninh Bao Duong, Philippe Fournier-Viger, Alioune Ngom


Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs.

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