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» kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets
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ISCI
2008
116views more  ISCI 2008»
14 years 9 months ago
Discovery of maximum length frequent itemsets
The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often ...
Tianming Hu, Sam Yuan Sung, Hui Xiong, Qian Fu
PKDD
2009
Springer
113views Data Mining» more  PKDD 2009»
15 years 4 months ago
Feature Selection for Density Level-Sets
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an eï¬...
Marius Kloft, Shinichi Nakajima, Ulf Brefeld
AI
2003
Springer
15 years 2 months ago
Efficient Mining of Indirect Associations Using HI-Mine
Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has be...
Qian Wan, Aijun An
92
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CIKM
2009
Springer
14 years 7 months ago
Diverging patterns: discovering significant frequency change dissimilarities in large databases
In this paper, we present a framework for mining diverging patterns, a new type of contrast patterns whose frequency changes significantly differently in two data sets, e.g., it c...
Aijun An, Qian Wan, Jiashu Zhao, Xiangji Huang
SDM
2009
SIAM
157views Data Mining» more  SDM 2009»
15 years 6 months ago
MUSK: Uniform Sampling of k Maximal Patterns.
Recent research in frequent pattern mining (FPM) has shifted from obtaining the complete set of frequent patterns to generating only a representative (summary) subset of frequent ...
Mohammad Al Hasan, Mohammed Javeed Zaki