Discovering Frequent Itemsets an Improved Algorithm of Bit Vector and Graph

Abstract:

Because of the weakness of traditional Apriori algorithm, this paper presents an improved algorithm for mining frequent itemsets, which constructs bit vector and graph, the algorithm deletes node and the adjacent edges according to the number of node’s edges, which need traverse graph to generate candidate itemsets and verify candidate itemset by bit vector. Experimental results show that the improved algorithm has better efficiency than Apriori algorithm.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 3747-3750
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.3747
• Online since: July 2014
• Authors: Nai Li Liu*
• Keywords: Association Rule, Bit Vector, Data Mining (DM), Frequent Item Set, Graph

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