Innovations in fuzzy clustering : theory and applications /

Innovations in fuzzy clustering : theory and applications /

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Bibliographic Details
Author / Creator:	Sato-Ilic, Mika.
Imprint:	Berlin ; New York : Springer, 2006.
Description:	1 online resource (xiii, 152 pages) : figure, table.
Language:	English
Series:	Studies in fuzziness and soft computing ; v. 205 Studies in fuzziness and soft computing ; v. 205.
Subject:	Fuzzy sets. Fuzzy logic. Fuzzy mathematics. Data mining. Soft computing. Data mining. Fuzzy logic. Fuzzy mathematics. Fuzzy sets. Soft computing.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11069012

Hidden Bibliographic Details
ISBN:	9783540343578 3540343571 3540343563 9783540343561 1280657200 9781280657207
Notes:	Includes bibliographical references and index. Print version record.
Summary:	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.
Other form:	Print version: Sato-Ilic, Mika. Innovations in fuzzy clustering. Berlin ; New York : Springer, 2006 3540343563 9783540343561

Description
Summary:	Clustering has been around for many decades and located itself in a uniquepositionasafundamentalconceptualandalgorithmiclandmark of data analysis. Almost since the very inception of fuzzy sets, the role and potential of these information granules in revealing and describing structureindatawasfullyacknowledgedandappreciated.Asamatter of fact, with the rapid growth of volumes of digital information, the role of clustering becomes even more visible and critical. Furthermore given the anticipated human centricity of the majority of artifacts of digitaleraandacontinuousbuildupofmountainsofdata,onebecomes fully cognizant of the growing role and an enormous potential of fuzzy sets and granular computing in the design of intelligent systems. In therecentyearsclusteringhasundergoneasubstantialmetamorphosis. Frombeinganexclusivelydata drivenpursuit,ithastransformeditself into a vehicle whose data centricity has been substantially augmented by the incorporation of domain knowledge thus giving rise to the next generation of knowledge-oriented and collaborative clustering. Interestingly enough, fuzzy clustering exhibits a dominant role in many developments of the technology of fuzzy sets including fuzzy modeling, fuzzy control, data mining, pattern recognition, and image processing. When browsing through numerous papers on fuzzy m- eling we can witness an important trend of a substantial reliance on fuzzy clustering being regarded as the general development tool. The same central position of fuzzy clustering becomes visible in pattern classi?ers and neurofuzzy systems. All in all, it becomes evident that further progress in fuzzy clustering is of vital relevance and bene?t to the overall progress of the area of fuzzy sets and their applications.
Physical Description:	1 online resource (xiii, 152 pages) : figure, table.
Bibliography:	Includes bibliographical references and index.
ISBN:	9783540343578 3540343571 3540343563 9783540343561 1280657200 9781280657207