Data Mining and Knowledge Discovery Handbook, 2 Edition part 127

Data Mining and Knowledge Discovery Handbook, 2 Edition part 127. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 1240 Oded Maimón and Abel Browarnik Taxonomies and ontologies NHECD uses at several stages manually prepared taxonomies. It is arguable that using an ontology of the Nanotox domain could enhance the quality of information extraction either textual graphic or tabular . On the other hand no Nanotox ontology exists. Research towards ontology learning could use NHECD results. In turn the learned ontology could improve information extraction implementing a kind of bootstrapping process. Data mining on the second NHECD product can have a strong influence on the ontology learning process. As a result the ontology can be further enhanced. References Arbel R. and Rokach L. Classifier evaluation under limited resources Pattern Recognition Letters 27 14 1619-1631 2006 Elsevier. Averbuch M. and Karson T. and Ben-Ami B. and Maimon O. and Rokach L. Contextsensitive medical information retrieval The 11th World Congress on Medical Informatics MEDINFO 2004 San Francisco CA September 2004 IOS Press pp. 282-286. Brin S. and Page L. 1998. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30 1-7 Apr. 1998 107-117. Cohen S. Rokach L. Maimon O. Decision Tree Instance Space Decomposition with Grouped Gain-Ratio Information Science Volume 177 Issue 17 pp. 3592-3612 2007. Maimon O. and Rokach L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study in Data Mining for Design and Manufacturing Methods and Applications D. Braha ed. Kluwer Academic Publishers pp. 311-336 2001. Maimon O. and Rokach L. Improving supervised learning by feature decomposition Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems Lecture Notes in Computer Science Springer pp. 178-196 2002. Maimon O. and Rokach L. Decomposition Methodology for Knowledge Discovery and Data Mining Theory and Applications Series in Machine Perception and Artificial Intelligence - Vol. 61 World Scientific Publishing ISBN .