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» Using an Interest Ontology for Improved Support in Rule Mini...
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SAC
2010
ACM
13 years 2 months ago
A study on interestingness measures for associative classifiers
Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Su...
Mojdeh Jalali Heravi, Osmar R. Zaïane
DAWAK
2000
Springer
13 years 12 months ago
Mining Frequent Binary Expressions
In data mining, searching for frequent patterns is a common basic operation. It forms the basis of many interesting decision support processes. In this paper we present a new type ...
Toon Calders, Jan Paredaens
DIS
2007
Springer
14 years 1 months ago
Literature Based Discovery Support System and Its Application to Disease Gene Identification
We present an interactive discovery support system, which for a given starting concept of interest, discovers new, potentially meaningful relations with other concepts that have no...
Dimitar Hristovski, Borut Peterlin, Saso Dzeroski,...
AAAI
2010
13 years 9 months ago
Constraint Programming for Data Mining and Machine Learning
Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose syste...
Luc De Raedt, Tias Guns, Siegfried Nijssen
TEC
2008
104views more  TEC 2008»
13 years 7 months ago
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data i...
Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-...