A TRANSACTION MAPPING ALGORITHM FOR MINING FREQUENT ITEMSETS
Abstract:
Affiliation Rule Mining is an extremely well-known Data mining method and it discovers connections among various substances of records. In this venture, we introduce a novel calculation for mining complete regular itemsets.
This calculation is alluded to as the TM (Transaction Mapping) calculation from here on. In this calculation, exchange ids of each itemset are mapped and packed to ceaseless exchange interims in an alternate space and the checking of itemsets is performed by converging these interim records in a profundity first request along with the lexicographic tree. At the point when the pressure coefficient ends up littler than the normal number of correlations for interims crossing point at a specific level, the calculation changes to exchange id convergence. We have assessed the calculation against two famous successive itemset mining calculations – FP-development and Eclat utilizing an assortment of informational collections with short and long continuous examples. Test information demonstrates that the TM calculation outflanks these two calculations.
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