Innovations in Fuzzy Clustering [electronic resource] :Theory and Applications / by Mika Sato-Ilic, Lakhmi C. Jain.
by Sato-Ilic, Mika [author.]; Jain, Lakhmi C [author.]; SpringerLink (Online service).
Material type:
BookSeries: Studies in Fuzziness and Soft Computing: 205Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XIII, 152 p. Also available online. online resource.ISBN: 9783540343578.Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TA640-643 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | TA329-348 (Browse shelf) | Available |
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| QR355-502 Kaposi Sarcoma Herpesvirus: New Perspectives | TA329-348 Introduction to Data Mining and its Applications | TA329-348 Chance Discoveries in Real World Decision Making | TA329-348 Innovations in Fuzzy Clustering | Reduced Order Systems | QA75.5-76.95 Computational Science – ICCS 2006 | QA75.5-76.95 Computational Science – ICCS 2006 |
to Fuzzy Clustering -- Fuzzy Clustering based Principal Component Analysis -- Fuzzy Clustering based Regression Analysis -- Kernel based Fuzzy Clustering -- Evaluation of Fuzzy Clustering -- Self-Organized Fuzzy Clustering.
There is a great interest in clustering techniques due to the vast amount of data generated in every field including business, health, science, engineering, aerospace, management and so on. It is essential to extract useful information from the data. Clustering techniques are widely used in pattern recognition and related applications. The research monograph presents the most recent advances in fuzzy clustering techniques and their applications. The following contents are included: Introduction to Fuzzy Clustering Fuzzy Clustering based Principal Component Analysis Fuzzy Clustering based Regression Analysis Kernel based Fuzzy Clustering Evaluation of Fuzzy Clustering Self-Organized Fuzzy Clustering This book is directed to the computer scientists, engineers, scientists, professors and students of engineering, science, computer science, business, management, avionics and related disciplines.
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