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» An Overview of Database Mining Techniques
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KDD
2009
ACM
219views Data Mining» more  KDD 2009»
14 years 8 months ago
Structured correspondence topic models for mining captioned figures in biological literature
A major source of information (often the most crucial and informative part) in scholarly articles from scientific journals, proceedings and books are the figures that directly pro...
Amr Ahmed, Eric P. Xing, William W. Cohen, Robert ...
IEAAIE
2009
Springer
14 years 2 months ago
Incremental Mining of Ontological Association Rules in Evolving Environments
The process of knowledge discovery from databases is a knowledge intensive, highly user-oriented practice, thus has recently heralded the development of ontology-incorporated data ...
Ming-Cheng Tseng, Wen-Yang Lin
ICDCS
2002
IEEE
14 years 1 months ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...
KDD
2003
ACM
149views Data Mining» more  KDD 2003»
14 years 8 months ago
Knowledge-based data mining
We describe techniques for combining two types of knowledge systems: expert and machine learning. Both the expert system and the learning system represent information by logical d...
Søren Damgaard, Sholom M. Weiss, Shubir Kap...
KDD
1999
ACM
108views Data Mining» more  KDD 1999»
14 years 11 days ago
Mining the Most Interesting Rules
Several algorithms have been proposed for finding the “best,” “optimal,” or “most interesting” rule(s) in a database according to a variety of metrics including confid...
Roberto J. Bayardo Jr., Rakesh Agrawal