基于WNegNodeset结构的加权频繁项集挖掘算法.

In the mining algorithm for frequent weighted itemsets based on WN-list ( NFWI ), mining weighted frequent itemsets ( FWI) is inefficient. To solve the problem, this paper proposed a frequent weighted itemsets mining algorithm ( NegNFWI) based on WNegNodeset structure. Firstly, this algorithm used the data structure of WNegNodeset, an extension of NegNodeset. The data structure employed a novel encoding model for nodes in bitmap weighted-tree ( BMW-tree) based on the bitmap representation of sets, and used bitwise operators to extract WNegNodesets of itemsets quickly, avoiding a large quantity of intersection operations. Secondly, this algorithm used diffsets strategy to calculate the weighted support degree of itemsets quickly, thus decreasing computing time. Finally, results from simulation experiments show that the proposed algorithm is efficient and feasible. [ABSTRACT FROM AUTHOR]