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» Methods for finding frequent items in data streams
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DAWAK
2004
Springer
14 years 1 months ago
Algorithms for Discovery of Frequent Superset, Rather than Frequent Subset
Abstract. In this paper, we propose a novel mining task: mining frequent superset from the database of itemsets that is useful in bioinformatics, e-learning systems, jobshop schedu...
Zhung-Xun Liao, Man-Kwan Shan
CIKM
2005
Springer
14 years 1 months ago
On the estimation of frequent itemsets for data streams: theory and experiments
In this paper, we devise a method for the estimation of the true support of itemsets on data streams, with the objective to maximize one chosen criterion among {precision, recall}...
Pierre-Alain Laur, Richard Nock, Jean-Emile Sympho...
ECML
2007
Springer
13 years 11 months ago
Finding Composite Episodes
Mining frequent patterns is a major topic in data mining research, resulting in many seminal papers and algorithms on item set and episode discovery. The combination of these, call...
Ronnie Bathoorn, Arno Siebes
ICDE
2000
IEEE
118views Database» more  ICDE 2000»
14 years 9 months ago
Mining Recurrent Items in Multimedia with Progressive Resolution Refinement
Despite the overwhelming amounts of multimedia data recently generated and the significance of such data, very few people have systematically investigated multimedia data mining. ...
Hua Zhu, Jiawei Han, Osmar R. Zaïane
KDD
2006
ACM
198views Data Mining» more  KDD 2006»
14 years 8 months ago
CFI-Stream: mining closed frequent itemsets in data streams
Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent clo...
Nan Jiang, Le Gruenwald