By Petra P. (ed.)
This booklet 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 desirous about complex information mining purposes in photo 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, photo mining, purposes in method keep an eye on and assurance, clustering and organization principles, telecomunications, and drugs and biotechnology.
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Whereas normal platforms study has had a substantial impression on examine within the social sciences, this influence has been quite often conceptual and has no longer served to supply the operational and methodological aids for study that are attainable. additionally, a lot of these systems-oriented instructions and effects which do impression social technological know-how study have constructed inde pendently and in piecemeal style in contemporary a long time.
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Additional resources for Advances In Data Mining: Applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
3. Corchado J. , Corchado E. , Fdez-Riverola F. and Glez-Bedia M. (2003) Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems. 5th International Conference on Case-Based Reasoning, Trondheim, Norway, June 23 to 26, 2003. Springer-Verlag 4. Freedman J. and Tukey J. (1974) A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers, (23): 881-890, 1974. 5. Fyfe C. and Corchado J. M. (2001) Automating the construction of CBR Systems using Kernel Methods.
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A set of feature descriptors have been proposed and rigorously in the MPEG-7 core experiments. We propose to extend the use of these descriptors onto semantics extraction from images and videos, so as to bridge the semantic gap in content-based image retrieval and enable multimedia data mining on semantics level. A computational framework consisting of a clustering process for feature mapping and a classification process for object extraction is introduced. We also present some preliminary results obtained from the experiments we have conducted.
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