Springer Science Business Data Mining and Knowledge Discovery, 11, 1-20, 2005 2005 Springer Science...  Home/Search
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Abstract: We present GenMax, a backtrack search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. We found GenMax to be a highly... (Update)
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BibTeX entry: (Update)
@misc{ business-data,
author = "Springer Science Business",
title = "Data Mining and Knowledge Discovery, 11, 1--20, 2005 c",
url = "citeseer.ist.psu.edu/751006.html" }
Citations (may not include all citations):400 Fast discovery of association rules (context) - Agrawal, Mannila et al. - 1996
249 Mining frequent patterns without candidate generation - Han, Pei et al. - 2000
108 Efficiently mining long patterns from databases (context) - Bayardo - 1998
56 Generating non-redundant association rules - Zaki - 2000
54 Pincer-search: A new algorithm for discovering the maximum f.. - Lin, Kedem - 1998
54 MAFIA: A maximal frequent itemset algorithm for transactiona.. (context) - Burdick, Calimlim et al. - 2001
25 Depth first generation of long patterns (context) - Agrawal, Aggarwal et al. - 2000
24 Fast vertical mining using Diffsets - Zaki, Gouda - 2003
21 CHARM: An efficient algorithm for closed itemset mining - Zaki, Hsiao - 2002
17 Discovering all most specific sentences - Gunopulos, Khardon et al. - 2003
4 Advances in frequent itemset mining implementations: Report .. - Goethals, Zaki - 2003
3 An algorithm for dynamic subset and intersection testing (context) - Yellin - 1994
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