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.

Knowledge Discovery in Databases (KDD) is the process of identifying valid, novel, useful, and understandable patterns from large datasets. Data Mining (DM) is the mathematical core of the KDD process, involving the inferring algorithms that explore the data, develop mathematical models and discover significant patterns (implicit or explicit) -which are the essence of useful knowledge. This detailed guide book covers in a succinct and orderly manner the methods one needs to master in order to pursue this complex and fascinating area.

Given the fast growing interest in the field, it is not surprising that a variety of methods are now available to researchers and practitioners. This handbook aims to organize all major concepts, theories, methodologies, trends, challenges and applications of Data Mining into a coherent and unified repository. This handbook provides researchers, scholars, students and professionals with a comprehensive, yet concise source of reference to Data Mining (and additional selected references for further studies).

This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.