Overview

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 prediction.

The accessibility and abundance of data today makes knowledge discovery and Data Mining a matter of considerable importance and necessity. Given the recent growth of the field, it is not surprising that a wide variety of methods is now available to the researchers and practitioners. No one method is superior to others for all cases. The handbook of Data Mining and Knowledge Discovery from Data aims to organize all significant methods developed in the field into a coherent and unified catalog; presents performance evaluation approaches and techniques; and explains with cases and software tools the use of the different methods.

The goals of this introductory chapter are to explain the KDD process, and to position DM within the information technology tiers. Research and development challenges for the next generation of the science of KDD and DM are also defined. The rationale, reasoning and organization of the handbook are presented in this chapter. In this...