By Petra P. (ed.)

ISBN-10: 3540301852

ISBN-13: 9783540301851

This ebook constitutes the completely refereed post-proceedings of the 4th commercial convention on info Mining, ICDM 2004, held in Leipzig, Germany on July 2004.The convention was once curious about complicated facts mining functions in photograph mining, medication and bioinformatics, administration and environmental regulate, and telecommunications. The 18 revised complete papers offered have been rigorously chosen in the course of rounds of reviewing and development. The papers are equipped in topical sections on case-based reasoning, snapshot mining, purposes in procedure keep watch over and assurance, clustering and organization ideas, telecomunications, and drugs and biotechnology.

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22 O. Raphaeli, J. Zahavi, and R. Kenett 24. : A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction, Expert Systems with Applications 23(3) (2002) 255-264. 25. , An Introduction to the Bootstrap , Chapman and Hall, New York(1993). 26. , Zacks S. :Modern Industrial Statistics: Design and Control of Quality and Reliability, Duxbury Press, San Francisco(1998). 27. Cheng, C. : A Fuzzy Inference System for Similarity Assessment in Case-Based Reasoning systems: An Application to Product Design, Mathematical and Computer Modeling 38 (2003) 385-394.

Johnson, D. W. , Englewood Cliffs, New York, 1991 6. L. Bobrowski, M. Topczewska: “Tuning of diagnosis support rules through visualizing data transformations”, pp. 15-23 in Medical Data Analysis, P. , Springer-Verlag Berlin , Heidelberg 2003 7. L. Bobrowski, H. Wasyluk, Diagnosis supporting rules of the Hepar system, pp. 1309 1313 in: MEDINFO 2001, Eds: V. L. Petel, R. Rogers, R. Haux, IOS Press, Amsterdam 2001 An IBR System to Quantify the Ocean’s Carbon Dioxide Budget Juan M. Corchado1, Emilio S.

The main part of the model is an IBR system that incorporates several connectionist techniques in the retrieval, reuse and retain stages. The IBR system uses the Cooperative Maximum Likelihood Hebbian Learning Model for clustering the Instance-base and for the retrieval of the instances most similar to the “problem instance”, due to its topology preserving properties [8]. The Cooperative Maximum Likelihood Hebbian Learning method is a novel approach that features both selection, in which the aim is to visualize and extract information from complex, and highly dynamic data.

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Advances In Data Mining: Applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications by Petra P. (ed.)


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