Title :
Mining maximal frequent itemsets for large scale transaction databases
Author :
Xia, Ran ; Yuan, Wei ; Ding, Sheng-Chao ; Liu, Juan ; Zhou, Huai-Bel
Author_Institution :
Adv. Res. Center for Sci. & Technol., Wuhan Univ., China
Abstract :
We present a graph-based algorithm, MFIminer, for mining maximal frequent itemsets (MFI) from transaction databases. Our method is especially efficient in large transaction databases because the performance is not sensitive to the quantity of transactions. MFIminer adopts a directed association graph to guide the mining task efficiently. It uses the technique of depth-first traversal and complete graph checking to achieve reduction of searching time. Performance study shows that MFIminer outperforms minmax, an algorithm to find MFI, in both speed and scalability property.
Keywords :
data mining; directed graphs; transaction processing; tree searching; very large databases; depth first traversal technique; directed association graph; graph based algorithm; large scale transaction databases; maximal frequent itemset mining; minmax algorithm; search time reduction; Association rules; Data mining; Electronic mail; Itemsets; Large-scale systems; Minimax techniques; Pattern analysis; Radio access networks; Scalability; Transaction databases;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1382007