Context-based Hierarchical Clustering for the Ontology Learning
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Web driven ontological concepts evaluation
IMSA'07 IASTED European Conference on Proceedings of the IASTED European Conference: internet and multimedia systems and applications
Spatial Data Mining with Uncertainty
Computational Intelligence and Security
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
Evaluating the Impact of Missing Data Imputation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Web driven ontological concepts evaluation
EurolMSA '07 Proceedings of the Third IASTED European Conference on Internet and Multimedia Systems and Applications
Density-based semi-supervised clustering
Data Mining and Knowledge Discovery
Contextual ontological concepts extraction
DS'06 Proceedings of the 9th international conference on Discovery Science
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Evaluation of ontology enhancement tools
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
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From the Publisher:"Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. It reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. Coverage of quality assessment begins with an introduction to cluster analysis and a comparison of the methods and approaches that may be used. The techniques and algorithms involved in other essential data mining tasks, such as classification and extraction of association rules, are also discussed together with a review of the quality criteria and techniques for evaluating the data mining results." "This book presents a general framework for assessing quality and handling uncertainty, which is based on tested concepts and theories. This framework forms the basis of an implementation tool, 'UMiner' which is introduced to the reader for the first time." Aimed at IT professionals involved with data mining and knowledge discovery, the work is supported with case studies from epidemiology that illustrate how the tool works in 'real-world' data mining projects. The book would also be of interest to final year undergraduates or post-graduate students looking at databases, algorithms, artificial intelligence and information systems particularly with regard to uncertainty and quality assessment.