New PDF release: Advances In Data Mining: Applications in Image Mining,

By Petra P. (ed.)

ISBN-10: 3540301852

ISBN-13: 9783540301851

This booklet constitutes the completely refereed post-proceedings of the 4th commercial convention on facts Mining, ICDM 2004, held in Leipzig, Germany on July 2004.The convention used to be fascinated with complex info mining purposes in picture mining, drugs and bioinformatics, administration and environmental keep an eye on, and telecommunications. The 18 revised complete papers provided have been conscientiously chosen in the course of rounds of reviewing and development. The papers are equipped in topical sections on case-based reasoning, picture mining, purposes in method regulate and assurance, clustering and organization principles, telecomunications, and drugs and biotechnology.

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The area occupied by the right points outlines the possibility to acquire knowledge at a later stage but at half level. This could mean an IMPROVE STRATEGY in the firm, where it needs to improve in what it already possesses. However, bottom right cloud represents the situation that the firm has to acquire the knowledge later but at a wide level. This means that the company should think about the idea of enlarging and growing, both in terms of new processes and new products. This is: GROWTH STRATEGY.

In: M. 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.

The result of the last stage depends strongly on the applied measure of the similarity between the cases. Let us assume that the labelled feature vectors from the learning sets (1) can be ranked in respect to the distances between the vectors and The ball be defined as: centred in and containing exactly K ranked vectors can Improving the K-NN Classification with the Euclidean Distance 25 The set defines the Euclidean neighbourhood of the point In accordance with the K-nearest neighbours (K-NN) rule, the object is allocated into this class where most of the labelled feature vectors from the neighbourhood belong [2]: where is the number of the vectors from the set contained in the ball The K-NN classification rule (5) depends on the number K of the neighbours taken into consideration and on the applied distance function The Euclidean distance function is most commonly used for the nearest neighbours classifiers Both the K number as well as the distance function could be optimised by minimisation of the error rate related to the given rule (5) [2], 3 Linear Transformations of the Learning Sets This Euclidean distance function can be modified by transformations of the feature vectors We are considering using the linear transformations of the feature vectors for this purpose where A is a matrix of dimension The Euclidean distance functions can be expressed as: (3) between the transformed vectors The Euclidean neighbourhood of the point in the transformed feature space can be defined by using the distances (7) in a similar manner to (4).

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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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