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» Finding Interesting Associations without Support Pruning
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ICDE
2000
IEEE
90views Database» more  ICDE 2000»
14 years 5 months ago
Finding Interesting Associations without Support Pruning
Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristid...
SIGMOD
2004
ACM
196views Database» more  SIGMOD 2004»
14 years 4 months ago
FARMER: Finding Interesting Rule Groups in Microarray Datasets
Microarray datasets typically contain large number of columns but small number of rows. Association rules have been proved to be useful in analyzing such datasets. However, most e...
Gao Cong, Anthony K. H. Tung, Xin Xu, Feng Pan, Ji...
JIS
2006
105views more  JIS 2006»
13 years 4 months ago
Automated support specification for efficient mining of interesting association rules
In recent years, the weakness of the canonical support-confidence framework for associations mining has been widely studied. One of the difficulties in applying association rules ...
Wen-Yang Lin, Ming-Cheng Tseng
ICDE
2004
IEEE
116views Database» more  ICDE 2004»
14 years 5 months ago
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting
Mining sequential patterns in large databases is an important research topic. The main challenge of mining sequential patterns is the high processing cost due to the large amount ...
Ding-Ying Chiu, Yi-Hung Wu, Arbee L. P. Chen
ICDE
2000
IEEE
96views Database» more  ICDE 2000»
14 years 5 months ago
Dynamic Miss-Counting Algorithms: Finding Implication and Similarity Rules with Confidence Pruning
Dynamic Miss-Countingalgorithms are proposed, which find all implication and similarity rules with confidence pruning but without support pruning. To handle data sets with a large...
Shinji Fujiwara, Jeffrey D. Ullman, Rajeev Motwani