Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining models as services on the internet
ACM SIGKDD Explorations Newsletter
Ontology-guided knowledge discovery in databases
Proceedings of the 1st international conference on Knowledge capture
Communications of the ACM
The Open Grid Services Architecture: Where the Grid Meets the Web
IEEE Internet Computing
DataSpace: A Data Web for the Exploratory Analysis and Mining of Data
Computing in Science and Engineering
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparative study for domain ontology guided feature extraction
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Ontology-Based Universal Knowledge Grid: Enabling Knowledge Discovery and Integration on the Grid
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
Data Mining: Next Generation Challenges and Future Directions
Data Mining: Next Generation Challenges and Future Directions
Developing an open knowledge discovery support system for network environment
CTS'05 Proceedings of the 2005 international conference on Collaborative technologies and systems
Toward a Fundamental Theory of Optimal Feature Selection: Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantizing for minimum average misclassification risk
IEEE Transactions on Neural Networks
Multi-agent system for customer relationship management with SVMs tool
International Journal of Intelligent Information and Database Systems
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The identification of valid, novel and interesting models from large volumes of data is the primary goal of Knowledge Discovery in Databases (KDD). In order to successfully achieve such a complex goal, many kinds of semantic information about the KDD and business domains is necessary. In this paper, we present an approach to the characterization of semantic domain information for a particular kind of KDD process: classification. In particular we show how, by estimating the properties of the true but unknown classification model, one can derive domain information on the classification problem at hand. We discuss how, by saving these properties with the data, users profit from this information and save time for experimenting with a lot of classifiers and parameters by accessing this knowledge.