An Efficient Approach for Incremental Mining Fuzzy Frequent Itemsets with FP-Tree

Abstract

Keeping the generated fuzzy frequent itemsets up-to-date and discovering the new fuzzy frequent itemsets are challenging problems in dynamic databases. In this paper, the classical H-struct structure is extended to mining fuzzy frequent itemsets. The extended H-mine algorithm can use any t-norm operator to calculate the support of fuzzy itemset. The FP-tree-based structure called the Initial-FP-tree and the New-FP-tree are built to maintain the fuzzy frequent itemsets in the original database and the new inserted transactions respectively. The strategy of incremental mining of fuzzy frequent itemsets is achieved by breath-first-traversing the Initial-FP-tree and the New-FP-tree. All of the fuzzy frequent itemsets in the updated database can be obtained by traversing the Initial-FP-tree. The experiments on real datasets show that the proposed approach runs faster than the batch extended H-mine algorithm. Comparing with the existing algorithm for incremental mining fuzzy frequent itemsets, the proposed approach is superior in terms of the execution time. The memory cost of the proposed approach is lower than that of the existing algorithm when the minimum support threshold is low.

An Improved Evaluation Methodology for Mining Association Rules

“…This method solves the problem of multifarious operation when the data are large. Huo et al [18] proposed an improved FP-tree algorithm to solve the defects of the Apriori algorithm. They set up the FP-tree-based (initial-FP-tree and new-FP-tree) structures to maintain fuzzy frequent itemsets in the original database and newly inserted transactions, respectively.…”