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» Approximate data mining in very large relational data
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KDD
1994
ACM
96views Data Mining» more  KDD 1994»
15 years 8 months ago
DICE: A Discovery Environment Integrating Inductive Bias
: Most of Knowledge Discovery in Database (KDD) systems are integrating efficient Machine Learning techniques. In fact issues in Machine Learning and KDD are very close allowing fo...
Jean-Daniel Zucker, Vincent Corruble, J. Thomas, G...
ICDM
2005
IEEE
168views Data Mining» more  ICDM 2005»
15 years 10 months ago
A Scalable Collaborative Filtering Framework Based on Co-Clustering
Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular ...
Thomas George, Srujana Merugu
PAKDD
2005
ACM
124views Data Mining» more  PAKDD 2005»
15 years 10 months ago
Finding Sporadic Rules Using Apriori-Inverse
We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the w...
Yun Sing Koh, Nathan Rountree
KDD
1995
ACM
140views Data Mining» more  KDD 1995»
15 years 8 months ago
Discovery and Maintenance of Functional Dependencies by Independencies
For semantic query optimization one needs detailed knowledgeabout the contents of the database. Traditional techniquesuse static knowledgeabout all possible states of the database...
Siegfried Bell
IDA
2000
Springer
15 years 4 months ago
Reducing redundancy in characteristic rule discovery by using integer programming techniques
The discovery of characteristic rules is a well-known data mining task and has lead to several successful applications. However, because of the descriptive nature of characteristic...
Tom Brijs, Koen Vanhoof, Geert Wets