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Abstract: A number of vertical mining algorithms have been proposed recently for association mining, which have shown to be very effective and usually outperform horizontal approaches. The main advantage of the vertical format is support for fast frequency counting via intersection operations on transaction ids (tids) and automatic pruning of irrelevant data. The main problem with these approaches is when intermediate results of vertical tid lists become too large for memory, thus affecting the algorithm ... (Update)
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BibTeX entry: (Update)
M. J. Zaki and K. Gouda. Fast Vertical Mining Using Diffsets. RPI Technical Report 01-1, 2001 11 http://citeseer.ist.psu.edu/zaki01fast.html More
@inproceedings{ zaki03fast,
author = {Mohammed J. Zaki and Karam Gouda},
title = {Fast vertical mining using diffsets},
booktitle = {KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining},
year = {2003},
isbn = {1-58113-737-0},
pages = {326--335},
location = {Washington, D.C.},
doi = {http://doi.acm.org/10.1145/956750.956788},
publisher = {ACM Press},
address = {New York, NY, USA},
url = {citeseer.ist.psu.edu/zaki01fast.html} }
Citations (may not include all citations):400 Fast discovery of association rules (context) - Agrawal - 1996
249 Mining frequent patterns without candidate generation - Han, Pei et al. - 2000
242 Dynamic itemset counting and implication rules for market ba.. - Brin, Motwani et al. - 1997
145 Sprint: A scalable parallel classifier for data mining - Shafer, Agrawal et al. - 1996
109 New algorithms for fast discovery of association rules - Zaki, Parthasarathy et al. - 1997
85 Discovering frequent closed itemsets for association rules - Pasquier, Bastide et al. - 1999
60 ective hash based algorithm for mining association rules (context) - Park, Chen et al. - 1995
56 Generating non-redundant association rules - Zaki - 2000
54 MAFIA: a maximal frequent itemset algorithm for transactiona.. (context) - Burdick, Calimlim et al. - 2001
54 Pincer-search: A new algorithm for discovering the maximum f.. - Lin, Kedem - 1998
53 ciently mining long patterns from databases (context) - Bayardo - 1998
40 Turbo-charging vertical mining of large databases - Shenoy - 2000
39 IEEE Transactions on Knowledge and Data Engineering (context) - Zaki, for - 2000
37 Discovering all the most specific sentences by randomized al.. - Gunopulos, Mannila et al. - 1997
36 cient algorithm for mining association rules in large databa.. (context) - Savasere, Omiecinski et al. - 1995
33 cient algorithm for closed itemset mining (context) - Zaki, Hsiao et al. - 2002
26 Mining association rules: Anti-skew algorithms - Lin, Dunham - 1998
21 cient algorithm for mining frequent closed itemsets (context) - Pei, Han et al. - 2000
20 ciently mining maximal frequent itemsets (context) - Gouda, Zaki - 2001
20 Depth First Generation of Long Patterns - Agrawal, Aggarwal et al. - 2000
16 Integrating association rule mining with databases: alternat.. (context) - Sarawagi, Thomas et al. - 1998
10 Data organization and access for e#cient data mining (context) - Dunkel, Soparkar - 1999
7 Sequential pattern mining using bitmaps (context) - Ayres, Gehrke et al. - 2002
4 Data Mining: Concepts and Techniuqes (context) - Han, Kamber - 2001
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