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CORR
2010
Springer
173views Education» more  CORR 2010»
13 years 5 months ago
Mining Multi-Level Frequent Itemsets under Constraints
Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multi...
Mohamed Salah Gouider, Amine Farhat
JCST
2008
119views more  JCST 2008»
13 years 7 months ago
Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy
This paper presents some new algorithms to efficiently mine max frequent generalized itemsets (g-itemsets) and essential generalized association rules (g-rules). These are compact ...
Daniel Kunkle, Donghui Zhang, Gene Cooperman
KDD
2007
ACM
177views Data Mining» more  KDD 2007»
14 years 8 months ago
Mining optimal decision trees from itemset lattices
We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal...
Élisa Fromont, Siegfried Nijssen
FIMI
2003
146views Data Mining» more  FIMI 2003»
13 years 9 months ago
Mining Frequent Itemsets using Patricia Tries
We present a depth-first algorithm, PatriciaMine, that discovers all frequent itemsets in a dataset, for a given support threshold. The algorithm is main-memory based and employs...
Andrea Pietracaprina, Dario Zandolin
DAMON
2009
Springer
14 years 2 months ago
Frequent itemset mining on graphics processors
We present two efficient Apriori implementations of Frequent Itemset Mining (FIM) that utilize new-generation graphics processing units (GPUs). Our implementations take advantage ...
Wenbin Fang, Mian Lu, Xiangye Xiao, Bingsheng He, ...