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» Mining Quantitative Associations in Large Database
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ICDE
2003
IEEE
146views Database» more  ICDE 2003»
14 years 11 months ago
Generalized Closed Itemsets for Association Rule Mining
The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent items...
Vikram Pudi, Jayant R. Haritsa
ESWA
2006
139views more  ESWA 2006»
13 years 9 months ago
An efficient data mining approach for discovering interesting knowledge from customer transactions
Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision ...
Show-Jane Yen, Yue-Shi Lee
SIGMOD
1997
ACM
134views Database» more  SIGMOD 1997»
14 years 2 months ago
Scalable Parallel Data Mining for Association Rules
One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consu...
Eui-Hong Han, George Karypis, Vipin Kumar
DKE
2007
119views more  DKE 2007»
13 years 9 months ago
Association rules mining using heavy itemsets
A well-known problem that limits the practical usage of association rule mining algorithms is the extremely large number of rules generated. Such a large number of rules makes the...
Girish Keshav Palshikar, Mandar S. Kale, Manoj M. ...
KES
2004
Springer
14 years 3 months ago
FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases
Association rule mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attribute lists using a ...
Jun Luo, Sanguthevar Rajasekaran