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Data Mining and Knowledge Discovery Handbook, 2 Edition part 16

Data Mining and Knowledge Discovery Handbook, 2 Edition part 16. 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. | 130 Irad Ben-Gal Runger G. Willemain T. Model-based and Model-free Control of Autocorrelated Processes Journal of Quality Technology 27 4 283-292 1995. Ruts I. Rousseeuw P. Computing Depth Contours of Bivariate Point Clouds In Computational Statistics and Data Analysis 23 153-168 1996. Schiffman S. S. Reynolds M. L. Young F. W. Introduction to Multidimensional Scaling Theory Methods and Applications. New York Academic Press 1981. Shekhar S. Chawla S. A Tour of Spatial Databases Prentice Hall 2002. Shekhar S. Lu C. T. Zhang P. Detecting Graph-Based Spatial Outlier Algorithms and Applications A Summary of Results In Proc. of the Seventh ACM-SIGKDD Conference on Knowledge Discovery and Data Mining SF CA 2001. Shekhar S. Lu C. T. Zhang P. Detecting Graph-Based Spatial Outlier Intelligent Data Analysis An International Journal 6 5 451-468 2002. Shekhar S. Lu C. T. Zhang P. A Unified Approach to Spatial Outliers Detection GeoIn-formatica an International Journal on Advances of Computer Science for Geographic Information System 7 2 2003. Wardell D.G. Moskowitz H. Plante R.D. Run-length distributions of special-cause control charts for correlated processes Technometrics 36 1 3-17 1994. Tukey J.W. Exploratory Data Analysis. Addison-Wesley 1977. Williams G. J. Baxter R. A. He H. X. Hawkins S. Gu L. A Comparative Study of RNN for Outlier Detection in Data Mining IEEE International Conference on Data-mining ICDM 02 Maebashi City Japan CSIRO Technical Report CMIS-02 102 2002. Williams G. J. Huang Z. Mining the knowledge mine The hot spots methodology for mining large real world databases In Abdul Sattar editor Advanced Topics in Artificial Intelligence volume 1342 of Lecture Notes in Artificial Intelligence 340-348 Springer 1997. Zhang N.F. A Statistical Control Chart for Stationary Process Data Technometrics 40 1 24-38 1998. Part II Supervised .