By Juan M. Corchado, M. Lourdes Borrajo, María A. Pellicer, J. Carlos Yáñez (auth.), Petra Perner (eds.)
The commercial convention on information Mining ICDM-Leipzig was once the fourth assembly in a chain of annual occasions which began in 2000, prepared by means of the Institute of desktop imaginative and prescient and utilized machine Sciences (IBaI) in Leipzig. The undertaking of the convention is to compile researchers and other people from so one can talk about jointly new developments and purposes in information mining. This 12 months a extensive spectrum of labor of alternative purposes was once provided starting from picture mining, drugs and biotechnology, administration and environmental keep an eye on, to telecommunications. in addition to that an commercial exhibition confirmed the profitable software of information mining tools through industries in numerous parts resembling scientific units, mass information administration platforms, information mining instruments, and so forth. in the course of the dialogue many initiatives have been encouraged resulting in new and joint paintings. The fruitful discussions, the trade of rules and the spirit of the convention made it a outstanding occasion for either side, and examine. we want to precise our appreciation to the reviewers for his or her distinctive and hugely expert paintings. We enjoy the assistance and figuring out of the editorial employees at Springer and specifically Alfred Hofmann, who supported the ebook of those complaints within the LNAI sequence. final, yet now not least, we want to thank all audio system, contributors and business exhibitors who contributed to the good fortune of the convention. we're anticipating welcoming you to ICDM 2005 (www.data-mini- forum.de) and to the recent paintings you'll current there.
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Additional resources for Advances in Data Mining: Applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications; 4th Industrial Conference on Data Mining, ICDM 2004, Leipzig, Germany, July 4 -7, 2004, Revised Selected Paper
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Fig. 1. Comparison of the error rate e(d) (37) for two 3-NN classifiers (K =3) with the error probability (33) of the Bayesian classification rule (27) for different distance d (32) values (error_B – the probability (37), error_1 - the error rate e(d) (37) for correlated learning sets error_2 - the error rate e(d) (37) for decorrelated sets 6 Conclusions Modification of the distance functions can improve the nearest neighbours KNN rule (4). We have demonstrated that replacement of the Euclidean distance (7) by the induced Mahalanobis distance (11) can lead to improvement of the K-NN rule (4) in the case of the normal model of data sets.
Veloso, A. ), Proceedings of 1st International Conference on CBR, Springer, Berlin(1995),43–54. 9. com 10. : Applying Case-Based Reasoning. Morgan Kaufman (1997). 11. McKenzie, D. : Classification by Similarity: An Overview of Statistical Methods of Case-Based Reasoning,: Computers in Human Behavior, 11-2 (1995) 273-288. 12. : Getting Organization and Human Ready for Information System eChallenges, Vienna (2004). 13. , Methods to Collect and Analyze Organizational Change Management Data: The BEST Approach, Colloquium of the Haifa University research Center of Organizational Behavior HR Management, 24/3/2004 14.