By Petra P. (ed.)
This publication constitutes the completely refereed post-proceedings of the 4th business convention on information Mining, ICDM 2004, held in Leipzig, Germany on July 2004.The convention used to be thinking about complicated information mining functions in picture mining, medication and bioinformatics, administration and environmental keep watch over, and telecommunications. The 18 revised complete papers offered have been rigorously chosen in the course of rounds of reviewing and development. The papers are geared up in topical sections on case-based reasoning, snapshot mining, functions in method regulate and coverage, clustering and organization principles, telecomunications, and drugs and biotechnology.
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Extra resources for Advances In Data Mining: Applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
And Lee D. (1998) The Rectified Gaussian Distribution, Advances in Neural Information Processing Systems, 10. 350 (1998). 4. Fyfe C. (1996) A Neural Network for PCA and Beyond, Neural Processing Letters, 6: 33-41. 5. Oja E. (1989) Neural Networks, Principal Components and Subspaces, International Journal of Neural Systems, 1:61-68. 6. Friedman J. and Tukey J. (1974) A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers, (23): 881-890. 7. Charles D. (1999) Unsupervised Artificial Neural Networks for the Identification of Multiple Causes in Data.
K). The learning set contains representatives (precedents) of the category The classification rules based on the nearest neighbours K-NN scheme  and on the Case Base Reasoning (CBR) approach  are central to us. In accordance with 1 This work was partially supported by the W/II/1/2004 and SPB-M (COST 282) grants from the University of Technology and by the 16/St/2004 grant from the Institute of Biocybernetics and Biomedical Engineering PAS. P. ): ICDM 2004, LNAI 3275, pp. 23–32, 2004. © Springer-Verlag Berlin Heidelberg 2004 24 L.
As an example, we can mention the automatic detection of machine failure on the base of noise measurements, or the computer systems for character recognition. The classification systems are designed on the base of data sets composed of numerical results from previous experiences (database systems) or by using sets of rules representing theoretical knowledge in a given domain (expert systems). ,K). The learning set contains representatives (precedents) of the category The classification rules based on the nearest neighbours K-NN scheme  and on the Case Base Reasoning (CBR) approach  are central to us.