Title :
A Two-Way Hybrid Algorithm for Maximal Frequent Itemsets Mining
Author :
Chen, Fu-zan ; Li, Min-qiang
Author_Institution :
Tianjin Univ., Tianjin
Abstract :
A new two-way-hybrid algorithm for mining maximal frequent itemsets is proposed. A flexible two-way-hybrid search method is given. The two-way-hybrid search begins the mining procedure in both the top-down and bottom-up directions at the same time. Moreover, information gathered in the bottom-up can be used to prune the search space in the other top-down direction. Some efficient decomposition and pruning strategies are implied in this method, which can reduce the original search space rapidly in the iterations. Experimental and analytical results are presented in the end.
Keywords :
data mining; set theory; tree data structures; tree searching; bottom-up search; maximal frequent itemset mining; pruning strategy; top-down search; tree data structure; Data mining; Frequency shift keying; Fuzzy systems; Itemsets; Optimization methods; Search methods; Transaction databases;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
DOI :
10.1109/FSKD.2007.130