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Abstract: Contents: 1. Introduction 2. Survey on Frequent Pattern Mining 3. Interactive Constrained Association Rule Mining 4. Upper Bounds (Update)
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BibTeX entry: (Update)
@phdthesis{ phd-goethals,
author = "Bart Goethals",
title = "Efficient Frequent Pattern Mining",
school = "University of Limburg, Belgium",
year = "2002",
url = "citeseer.ist.psu.edu/goethals02efficient.html" }
Citations (may not include all citations):1002 Programming Language (context) - Stroustrup - 1997
921 Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993
910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994
910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994
696 UCI Repository of machine learning databases (context) - Blake, Merz - 1998
641 The anatomy of a large-scale hypertextual web search engine - Brin, Page - 1998
474 Advances in Knowledge Discovery and Data Mining (context) - Fayyad, Piatetsky-Shapiro et al. - 1996
400 Fast discovery of association rules (context) - Agrawal, Mannila et al.
268 Mining generalized association rules - Srikant, Agrawal
262 From data mining to knowledge discovery: An overview (context) - Fayyad, Piatetsky-Shapiro et al.
249 Mining frequent patterns without candidate generation: A fre.. - Han, Pei et al. - 2003
249 Mining frequent patterns without candidate generation - Han, Pei et al.
242 Dynamic itemset counting and implication rules for market ba.. - Brin, Motwani et al. - 1997
225 Data Mining: Concepts and Techniques (context) - Han, Kamber - 2001
189 Sampling large databases for association rules - Toivonen
161 Exploratory mining and pruning optimizations of constrained .. - Ng, Lakshmanan et al.
121 Mining association rules with item constraints - Srikant, Vu et al.
109 New algorithms for fast discovery of association rules - Zaki, Parthasarathy et al.
107 Principles of Data Mining (context) - Hand, Mannila et al. - 2001
100 Levelwise search and borders of theories in knowledge discov.. - Mannila, Toivonen - 1997
89 A new SQL-like operator for mining association rules - Meo, Psaila et al.
88 A database perspective on knowledge discovery - Imielinski, Mannila - 1996
85 Discovering frequent closed itemsets for association rules - Pasquier, Bastide et al. - 1999
85 Constraint-based rule mining on large (context) - Bayardo, Agrawal et al. - 1999
66 DMQL: A data mining query language for relational databases - Han, Fu et al. - 1996
63 A tree projection algorithm for generation of frequent items.. - Agarwal, Aggarwal et al. - 2001
60 ective hash based algorithm for mining association rules (context) - Park, Chen et al. - 1995
57 DBMiner: A system for mining knowledge in large relational d.. - Han, Fu et al. - 1996
55 Cambridge University Press (context) - Bollobas - 1986
54 Real world performance of association rule algorithms - Zheng, Kohavi et al. - 2001
54 An apriori-based algorithm for mining frequent substructures.. - Inokuchi, Washio et al.
53 ciently mining long patterns from databases (context) - Bayardo
52 Frequent subgraph discovery - Kuramochi, Karypis
48 Optimization of constrained frequent set queries with 2-vari.. - Lakshmanan, Ng et al.
45 Online association rule mining - Hidber
40 Turbo-charging vertical mining of large databases - Shenoy, Haritsa et al.
39 IEEE Transactions on Knowledge and Data Engineering (context) - Zaki, for - 2000
37 Mining frequent itemsets with convertible constraints - Pei, Han et al. - 2001
36 cient algorithm for mining association rules in large databa.. (context) - Savasere, Omiecinski et al.
36 The number of simplices in a complex (context) - Kruskal - 1963
34 and machine learning (context) - Gunopulos, Khardon et al. - 1997
34 Automating the analysis and cataloging of sky surveys (context) - Fayyad, Djorgovski et al.
33 cient algorithm for closed itemset mining (context) - Zaki, Hsiao et al. - 2002
33 Mining all non-derivable frequent itemsets - Calders, Goethals
32 MSQL: A query language for database mining (context) - Imielinski, Virmani - 1999
32 Inductive databases and condensed representations for data m.. - Mannila - 1997
31 A theorem of finite sets (context) - Katona - 1968
29 Selecting the right interestingness measure for association .. - Tan, Kumar et al.
25 Can we push more constraints into frequent pattern mining (context) - Pei, Han
22 A condensed representation to find frequent patterns - Bykowski, Rigotti - 2001
20 Depth first generation of long patterns - Agarwal, Aggarwal et al.
19 Free-sets: A condensed representation of boolean data for th.. - Boulicaut, Bykowski et al. - 2003
19 ciently mining frequent trees in a forest (context) - Zaki
14 A new and versatile method for association generation - Amir, Feldman et al. - 1997
13 Enhancing the apriori algorithm for frequent set counting - Orlando, Palmerini et al. - 2001
13 A new short proof for the Kruskal--Katona theorem (context) - Frankl - 1984
11 cient algorithms for discovering association rules (context) - Mannila, Toivonen et al. - 1994
10 Fast vertical mining using di#sets (context) - Zaki - 2001
10 Using a knowledge cache for interactive discovery of associa.. - Nag, Deshpande et al. - 1999
9 cient mining of constrained correlated sets (context) - Grahne, Lakshmanan et al. - 2000
9 Mining association rules: Deriving a superior algorithm by a.. - Hipp, Guntzer et al.
9 Adaptive and resource-aware mining of frequent sets - Orlando, Palmerini et al. - 2002
7 cient search for association rules (context) - Webb
7 A tight upper bound on the number of candidate patterns - Geerts, Goethals et al.
7 Induction of association rules: Apriori implementation (context) - Borgelt, Kruse - 2002
7 Discovery of relational association rules - Dehaspe, Toivonen - 2001
6 Fast algorithms for mining association rules and sequential .. (context) - Srikant - 1996
6 On supporting interactive association rule mining - Goethals, Van den Bussche - 2000
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4 A data mining framework for optimal product selection in ret.. - Brijs, Goethals et al.
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