Knowledge Discovery today is a significant study and research area. In finding answers to many research questions in this area, the ultimate hope is that knowledge can be extracted from various forms of data around us. This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.
The Self-Organizing Map (SOM)  is an excellent tool for data survey because it has prominent visualization properties. It creates a set of prototype vectors representing the data set and carries out a topology preserving projection of the prototypes from the d-dimensional input space onto a low-dimensional grid (two dimensions in this article). This ordered grid can be used as a convenient visualization surface for showing different features of the data.