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Abstract: The resource requirements of frequent pattern mining algorithms depend mainly on the length distribution of the mined patterns in the database. Synthetic databases, which are used to benchmark performance of algorithms, tend to have distributions far different from those observed in real datasets. In this paper we focus on the problem of synthetic database generation and propose algorithms to effectively embed within the database, any given set of maximal pattern collections, and make the... (Update)
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BibTeX entry: (Update)
@misc{ university-distributionbased,
author = "Ganesh Ramesh University",
title = "Distribution-Based Synthetic Database Generation Techniques for Itemset
Mining",
url = "citeseer.ist.psu.edu/751196.html" }
Citations (may not include all citations):910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994
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225 Data mining: concepts and techniques (context) - Han, Kamber - 2000
161 Exploratory mining and pruning optimizations of constrained .. - Ng, Lakshmanan et al. - 1998
109 New algorithms for fast discovery of association rules - Zaki, Parthasarathy et al. - 1997
108 Efficiently mining long patterns from databases (context) - Bayardo - 1998
100 Levelwise search and borders of theories in knowledge discov.. - Mannila, Toivonen - 1997
60 Privacy preserving mining of association rules - Evfimievski, Srikant et al. - 2002
54 Real world performance of association rule algorithms - Zheng, Kohavi et al. - 2001
25 A simple algorithm for finding frequent elements in streams .. - Karp - 2003
24 Efficiently mining maximal frequent itemsets - Gouda, Zaki - 2001
22 A condensed representation to find frequent patterns - Bykowski - 2001
17 Discovering all most specific sentences - Gunopulos - 2003
4 Feasible itemset distributions in data mining: Theory and ap.. (context) - Ramesh, Maniatty et al. - 2003
3 Indexing and Data Access Methods for Database Mining - Ramesh, Maniatty et al. - 2002
2 Privacy preserving data mining of association rules on verti.. (context) - Kantarcioglu, Clifton - 2003
1 The Complexity of Mining Maximal Frequent Itemsets and Maxim.. (context) - Yang - 2004
1 Mining all non-derivable itemsets (context) - Calders, Goethals - 2002
1 A tight upper bound on the number of candidate patterns (context) - Goethals, Geerts et al. - 2001
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