TAILIEUCHUNG - Data Mining and Knowledge Discovery Handbook, 2 Edition part 53

Data Mining and Knowledge Discovery Handbook, 2 Edition part 53. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 500 Swagatam Das and Ajith Abraham References A. Abraham C. Grosan and V. Ramos Eds. 2006 Swarm Intelligence and Data Mining Studies in Computational Intelligence Springer Verlag Germany pages 270 ISBN 3540-34955-3. Ahmed MN Yaman SM Mohamed N Farag AA and Moriarty TA 2002 Modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21 pp. 193199. Azzag H Guinot C and Venturini G 2006 Data and text mining with hierarchical clustering ants in Swarm Intelligence in Data Mining Abraham A Grosan C and Ramos V Eds Springer pp. 153-186. Bandyopadhyay S and Maulik U 2000 Genetic clustering for automatic evolution of clusters and application to image classification Pattern Recognition 35 pp. 1197-1208. Beni G and Wang U 1989 Swarm intelligence in cellular robotic systems. In NATO Advanced Workshop on Robots and Biological Systems Il Ciocco Tuscany Italy. Bensaid AM Hall LO Bezdek Clarke LP 1996 Partially supervised clustering for image segmentation. Pattern Recognition vol. 29 pp. 859-871. Bezdek JC 1981 Pattern recognition with fuzzy objective function algorithms. New York Plenum. Bonabeau E Dorigo M and Theraulaz G 1999 Swarm Intelligence From Natural to Artificial Systems. Oxford University Press New York. Brucker P 1978 On the complexity of clustering problems. Beckmenn M and Kunzi HP Eds. Optimization and Operations Research Lecture Notes in Economics and Mathematical Systems Berlin Springer pp. 45-54. Calinski RB and Harabasz J 1975 Adendrite method for cluster analysis Commun. Statistics 1 27. Chou CH Su MC and Lai E 2004 A new cluster validity measure and its application to image compression Pattern Analysis and Applications 7 2 205-220. Clark MC Hall LO Goldgof DB Clarke LP Velthuizen RP and Silbiger MS 1994 MRI segmentation using fuzzy clustering techniques. IEEE Eng Med Biol 13 . Clerc M and Kennedy J. The particle swarm - explosion stability and convergence in a multidimensional complex

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