Accelerating Closed Frequent Itemset Mining by Elimination of Null Transactions
نویسنده
چکیده
The mining of frequent itemsets is often challenged by the length of the patterns mined and also by the number of transactions considered for the mining process. Another acute challenge that concerns the performance of any association rule mining algorithm is the presence of „null‟ transactions. This work proposes a closed frequent itemset mining algorithm viz., Closed Frequent Itemset Mining and Pruning (CFIM-P) algorithm using the sub-itemset pruning strategy. CFIM-P algorithm has attempted to eliminate redundant patterns by pruning closed frequent sub-itemsets. An attempt has even been made towards eliminating the null transactions by using Vertical Data Format representation technique for finding the frequent itemsets. Keywords— Data Mining, Frequent Pattern Growth (FP) tree, Frequent Itemsets, Closed Itemsets, Frequent Patterns
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