Advances In Data Mining: Applications in Image Mining, by Petra P. (ed.)

By Petra P. (ed.)

This ebook 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 taken with complex info mining purposes in photograph mining, medication and bioinformatics, administration and environmental keep watch over, and telecommunications. The 18 revised complete papers offered have been conscientiously chosen in the course of rounds of reviewing and development. The papers are equipped in topical sections on case-based reasoning, photograph mining, purposes in strategy keep an eye on and coverage, clustering and organization principles, telecomunications, and medication and biotechnology.

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As a final conclusion we could say that we have presented and applied a model called Beta-Cooperative Learning as a novel and robust tool to identify critical situations that allow firms to take decisions about acquisition, transfer and updating processes about knowledge management. We have applied some other methods such as PCA [5] [6] or MLHL [7], but BetaCooperative Learning provides more sparse projections [8] and captures a form of global ordering in the data set. The initial results show that the system presented is a reliable tool for classifying the different situations (clusters) which the firm may face and to identify whether the firm is at a situation that requires it to take decisions about acquisition, transfer and updating processes about knowledge management.

A Beta-Cooperative CBR System for Constructing a Business Management Model 47 4 The Beta Cooperative Case-Based Reasoning System The case-based reasoning developed assists experts in the decision processes taken, and helps to improve business processes. Case-based reasoning systems are used to solve new problems by adapting solutions that were used to solve previous similar problems [10]. The operation of a CBR system involves the adaptation of old solutions to match new experiences, using past cases to explain new situations, using previous experience to formulate new solutions, or reasoning from precedents to interpret a similar situation.

9. No. 3. 2. Pal S. , Dillon T. S. and Yeung D. S. (2000) Soft Computing in Case-based Reasoning. ). K. 3. Seung H. , Socci N. , 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.

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