Data Mining and Knowledge Discovery Handbook, 2 Edition part 3. 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. | XX List of Contributors Evgenii Vityaev Institute of Mathematics Russian Academy of Sciences Russia Michail Vlachos IBM T. J. Watson Research Center USA loannis Vlahavas Dept. of Informatics Aristotle University of Thessaloniki 54124 Greece Haixun Wang IBM T. J. Watson Research Center USA Wei Wang Department of Computer Science University of North Carolina at Chapel Hill USA Geoffrey I. Webb Faculty of Information Technology Monash University Australia Gary M. Weiss Department of Computer and Information Science Fordham University USA Ian H. Witten Department of Computer Science University of Waikato New Zealand Jacob Zahavi The Wharton School University of Pennsylvania USA Arkady Zaslavsky Centre for Distributed Systems and Software Engineering Monash University Peter G. Zhang Department of Managerial Sciences Georgia State University USA Pusheng Zhang Department of Computer Science and Engineering University of Minnesota USA Qingyu Zhang Arkansas State University Department of Computer and Info. Tech. Jonesboro AR 72467-0130 USA Ruofei Zhang Yahoo Inc. Sunnyvale CA 94089 Zhongfei Mark Zhang SUNY Binghamton NY 13902-6000 Blaz Zupan Faculty of Computer and Information Science University of Ljubljana Slovenia 1 Introduction to Knowledge Discovery and Data Mining Oded Maimon1 and Lior Rokach2 1 Department of Industrial Engineering Tel-Aviv University Ramat-Aviv 69978 Israel maimon@ 2 Department of Information System Engineering Ben-Gurion University Beer-Sheba Israel liorrk@ Knowledge Discovery in Databases KDD is an automatic exploratory analysis and modeling of large data repositories. KDD is the organized process of identifying valid novel useful and understandable patterns from large and complex data sets. Data Mining DM is the core of the KDD process involving the inferring of algorithms that explore the data develop the model and discover previously unknown patterns. The model is used for understanding phenomena from the data analysis and
