Classification and Clustering for Knowledge Discovery
Contributor(s): Halgamuge, Saman K [editor.] | Wang, Lipo [editor.] | SpringerLink (Online service).
Material type:
BookSeries: Studies in Computational Intelligence: 4Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: XII, 356 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540324041.Subject(s): Engineering | Operations research | Decision making | Artificial intelligence | Computer graphics | Computer-aided engineering | Applied mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Computer Imaging, Vision, Pattern Recognition and Graphics | Computer-Aided Engineering (CAD, CAE) and Design | Applications of Mathematics | Operation Research/Decision TheoryDDC classification: 519 Online resources: Click here to access online
In:
Springer eBooksSummary: 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.
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK7672 |
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.
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