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» Enhanced mining of association rules from data cubes
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SBACPAD
2003
IEEE
180views Hardware» more  SBACPAD 2003»
14 years 1 months ago
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth...
WWW
2001
ACM
14 years 9 months ago
WebLQM : A Web Community Examiner
WebLQM is a system with capabilities to locate, query and mine web communities on the Internet. WebLQM has a special way to define the World Wide Web, its contents and relations. ...
Jesús Ubaldo Quevedo, Shou-Hsuan Stephen Hu...
ADC
2003
Springer
182views Database» more  ADC 2003»
14 years 1 months ago
CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expens...
Yudho Giri Sucahyo, Raj P. Gopalan
SIGMOD
1999
ACM
112views Database» more  SIGMOD 1999»
14 years 27 days ago
A New Method for Similarity Indexing of Market Basket Data
In recent years, many data mining methods have been proposed for finding useful and structured information from market basket data. The association rule model was recently propos...
Charu C. Aggarwal, Joel L. Wolf, Philip S. Yu
DKE
2008
124views more  DKE 2008»
13 years 8 months ago
A MaxMin approach for hiding frequent itemsets
In this paper, we are proposing a new algorithmic approach for sanitizing raw data from sensitive knowledge in the context of mining of association rules. The new approach (a) rel...
George V. Moustakides, Vassilios S. Verykios