Classification and Clustering for Knowledge Discovery

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. The first 5 chapters of the book provide a collection of recent research on distributed clustering, the 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. The next 6 chapters are about methods of supervised data analysis. A book on knowledge discovery must have the essence of the various applications. The last 11 chapters include applications in health, safety, commerce, mechatronics, sensor networks, and telecommunications.