A method for mining top-rank- k frequent closed itemsets
Loan T. T. Nguyen, Truc Trinh, Ngoc Thanh Nguyên, Bay Vo · Journal of Intelligent & Fuzzy Systems · 2016
Mining frequent closed itemsets (FCIs) is important in mining non-redundant (minimal) association rules. Therefore, many algorithms have been developed for mining FCIs with reduced mining time and memory usage. For mining FCIs, algorithms use the minimum support threshold, minSup , to prune itemsets. However, using a fixed minSup is not suitable for mining top-rank- k FCIs. A large threshold will lead to a small number of generated FCIs, leading to insufficient FCIs to query when k is large. On the other hand, a small minSup will generate a huge number of generated FCIs, leading to large runtimes and high memory usage. In this paper, we propose a method for mining top-rank- k FCIs without using a fixed minimum support threshold. A strategy is first used to eliminate 1-items that cannot generate FCIs belonging to top-rank- k FCIs. Next, based on the set of candidate 1-items, we propose TRK-FCI, a DCI-Plus-based algorithm, for mining top-rank- k FCIs. In the process of mining top-rank- k FCIs, TRK-FCI automatically increases minSup according to the mined FCIs, efficiently pruning itemsets that cannot belong to top-rank- k FCIs. We also modify the dynamic bit vector (DBV) structure and apply it to reduce memory usage and runtime in the TRK-FCI-DBV algorithm. Experimental results show that TRK-FCI-DBV is more efficient than TRK-FCI for various databases.