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Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications (Studies in Fuzziness and Soft Computing (229)) 🔍
Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda (auth.) Springer-Verlag Berlin Heidelberg, Studies in Fuzziness and Soft Computing, Studies in Fuzziness and Soft Computing 229, 1, 2008
英语 [en] · PDF · 5.1MB · 2008 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/scihub/zlib · Save
描述
The main subject of this book is the fuzzy __c__-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy __c__-means is that most methodology and application studies in fuzzy clustering use fuzzy __c__-means, and hence fuzzy __c__-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy __c__-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy __c__-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy __c__-means become __complete__ by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.
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lgli/Algorithms For Fuzzy Clustering - Methods In C-Means Clustering With Applications Sadaaki Miyamoto (Springer 2008 244S)ISBN978-3-540-25381-5.pdf
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lgrsnf/Algorithms For Fuzzy Clustering - Methods In C-Means Clustering With Applications Sadaaki Miyamoto (Springer 2008 244S)ISBN978-3-540-25381-5.pdf
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scihub/10.1007/978-3-540-78737-2.pdf
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zlib/Computers/Computer Science/Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda (auth.)/Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications_634663.pdf
备选标题
The Fuzzification of Systems: The Genesis of Fuzzy Set Theory and its Initial Applications - Developments up to the 1970s (Studies in Fuzziness and Soft Computing (216))
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Fuzzy Logic: A Spectrum of Theoretical & Practical Issues (Studies in Fuzziness and Soft Computing) (Studies in Fuzziness and Soft Computing)
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Classic Works of the Dempster-Shafer Theory of Belief Functions (Studies in Fuzziness and Soft Computing (219))
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Fuzzy Choice Functions: A Revealed Preference Approach (Studies in Fuzziness and Soft Computing)
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Classic works of the Dempster-Shafer theory of belief functions with 43 tables
备选作者
Miyamoto, Sadaaki, Ichihashi, Hidetomo, Honda, Katsuhiro
备选作者
Paul P. Wang, Da Ruan And Etienne E. Kerre
备选作者
Ronald R. Yager; Liping Liu
备选作者
Irina Georgescu
备选作者
Seising, Rudolf
备选作者
Rudolf Seising
备用出版商
Springer Spektrum. in Springer-Verlag GmbH
备用出版商
Steinkopff. in Springer-Verlag GmbH
备用出版商
Springer-Verlag New York, LLC
备用版本
Studies in fuzziness and soft computing, Vol. 219, Berlin Heidelberg New York NY, 2008
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Studies in Fuzziness and Soft Computing, 216, Online-ausg, Berlin, Heidelberg, 2007
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Studies in fuzziness and soft computing, v. 216, Berlin ; New York, ©2007
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Studies in fuzziness and soft computing, 229, Berlin, Heidelberg, 2008
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Studies in fuzziness and soft computing -- 229, Berlin, Germany, 2008
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Studies in Fuzziness and Soft Computing, 2007 edition, August 8, 2007
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Studies in fuzziness and soft computing, v. 214, Berlin, ©2007
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Studies in fuzziness and soft computing, v. 215, Berlin, ©2007
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Studies in fuzziness and soft computing, v. 229, Berlin, ©2008
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Springer Nature, Berlin, Heidelberg, 2008
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Springer Nature, Berlin, Heidelberg, 2007
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1 edition, April 25, 2007
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1 edition, July 31, 2007
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1 edition, May 1, 2007
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2008, 2008-04-15
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Germany, Germany
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2008, US, 2008
元数据中的注释
0
元数据中的注释
lg586641
元数据中的注释
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元数据中的注释
Includes bibliographical references (p. [235]-243) and index.
备用描述
Why Fuzzy Logic? : A Spectrum Of Theoretical And Pragmatics Issues / Paul P. Wang, Da Ruan, And Etienne E. Kerre -- On Fuzzy Set Theories / Ana Pradera [and Others] -- Uninorm Basics / János Fodor And Bernard De Baets -- Structural Interpolation And Approximation With Fuzzy Relations : A Study In Knowledge Reuse / Witold Pedrycz -- On Fuzzy Logic And Chaos Theory : From An Engineering Perspective / Zhong Li And Xu Zhang -- Upper And Lower Values For The Level Of Fuzziness In Fcm / Ibrahim Ozkan And I.b. Turksen -- Mathematical Modeling Of Natural Phenomena : A Fuzzy Logic Approach / Michael Margaliot -- Mathematical Fuzzy Logic In Modeling Of Natural Language Semantics / Vilém Novák -- Analytical Theory Of Fuzzy If-then Rules With Compositional Rule Of Inference / Irina Perfilieva -- Fuzzy Logic And Ontology-based Information Retrieval / Mustapha Baziz [and Others] -- Real-world Fuzzy Logic Applications In Data Mining And Information Retrieval / Bernadette Bouchon-meunier [and Others] -- Gene Regulatory Network Modeling : A Data Driven Approach / Yingjun Cao, Paul P. Wang, And Alade Tokuta -- An Abstract Approach Toward The Evaluation Of Fuzzy Rule Systems / Siegfried Gottwald -- Nuclear Reactor Power Control Using State Feedback With Fuzzy Logic / Jorge S. Benítez-read, J. Humberto Pérez-cruz And Da Ruan -- The Fusion Of Genetic Algorithms And Fuzzy Classification For Transient Identification / Enrico Zio And Piero Baraldi -- The Role Of Fuzziness In Decision Making / Javier Montero, Victoria López And Daniel Gómez -- Fuzzy Linear Bilevel Optimatization : Solution Concepts, Approaches And Applications / Guangquan Zhang, Jie Lu And Tharam Dillon -- Fuzzy Predictive Earth Analysis Constrained By Heuristics Applied To Stratigraphic Modeling / Jeffrey D. Warren, Robert V. Demicco And Louis R. Bartek -- Fuzzy Logic For Modeling The Management Of Technology / André Maïsseu And Benoît Maïsseu. Paul P. Wang, Da Ruan, Etienne E. Kerre (eds.). Includes Bibliographical References And Index.
备用描述
In order to properly characterize the content of this book, it is important to clarify ?rst the intended meaning of its title Fuzzy Logic. This clari?cation is needed since the term “fuzzy logic,” as currently used in the literature, is viewed either in a narrow sense or in a broad sense. In the narrow sense, fuzzy logic is viewed as an area devoted to the formal development, in a u- ?ed way, of the various logical systems of many-valued logic. It is concerned withformalizingsyntactic aspects(basedonthenotionofproof)andsemantic aspects (based on the notion oftruth) of the variouslogical calculi. In order to be acceptable, each of these logical calculi must be sound (provability implies truth) and complete (truth implies provability). The most representativep- lication of fuzzy logic in this sense is, in my opinion, the classic book by Peter Hajek [1]. When the term “fuzzy logic” is viewed in the broad sense, it refers to an extensive agenda whose primary aim is to utilize the apparatus of fuzzy set theoryfordevelopingsoundconcepts,principles,andmethodsforrepresenting and dealing with knowledge expressed by statements in natural language. Although workin fuzzy logicin the broadsense is not directly concernedwith the issues that are investigated under fuzzy logic in the narrow sense, the importance of the latter is that it provides the former with solid theoretical foundations. After examining the content of this book, it is easy to conclude that its title,FuzzyLogic, referstofuzzylogicinthebroadsense. Thisisconsistent,by and large, with the usual meaning of the term “fuzzy logic” in the literature.
Erscheinungsdatum: 20.06.2007
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Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajortechniqueofclusteringingeneral,regardlesswhetheroneisinterested in fuzzy methods or not. Moreover recent advances in clustering techniques are rapid and we requirea new textbook that includes recent algorithms.We should also note that several books have recently been published but the contents do not include some methods studied herein.
Erscheinungsdatum: 15.04.2008
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A main topic in welfare economics is the rational behaviour of a consumer when, faced with various prices and incomes, he has to make a choice. The theory of consumption establishes the framework in which the rationality of consumers is de?ned and the principle on which it is based. By [109], “the rationality of a consumer may be described by postulating that a consumer has a de?nite preference over all conceivable commodity bundles and that he chooses those commodity bundles that are optimal with respect to his preference subject to budgetary constraints”. Samuelson’s theory of revealed preference expresses the rationality of a consumer in terms of some preference relation associated with a demand fu- tion. The foundation of this theory is built on The Weak Axiom of Consumer Behavior [87] and on The Strong Axiom of Consumer Behavior [63]. The s- ond axiom assures that the demand function can be reconstructed from a revealed preference relation. To make a rational choice is a more general problem that goes beyond the theme of consumer. In economics, social life, medicine, psychology, etc. there are several cases when an agent has to make rational decisions. For instance, when the members of a society vote di?erent candidates in an election, a plausible hypothesis is that, having a desideratum, each of them is rational in the act of choice.
Erscheinungsdatum: 26.04.2007
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This book brings together a collection of classic research papers on the Dempster-Shafer theory of belief functions. By bridging fuzzy logic and probabilistic reasoning, the theory of belief functions has become a primary tool for knowledge representation and uncertainty reasoning in expert systems. This book will serve as the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. From over 120 nominated contributions, the editors selected 30 papers, which are widely regarded as classics and will continue to make impacts on the future development of the field. The contributions are grouped into seven sections, including conceptual foundations, theoretical perspectives, theoretical extensions, alternative interpretations, and applications to artificial intelligence, decision-making, and statistical inferences. The book also includes a foreword by Dempster and Shafer reflecting the development of the theory in the last forty years, and an introduction describing the basic elements of the theory and how each paper contributes to the field.
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This book exclusively surveys the active on-going research of the current maturity of fuzzy logic over the last four decades. Many world leaders of fuzzy logic have enthusiastically contributed their best research results into five theoretical, philosophical and fundamental sub areas and nine distinctive applications, including two PhD dissertations from two world class universities dealing with cutting-edge research areas of bioinformatics and geological science. Beyond the scope of survey and collection of the book, one important spin off is the emerging and recognition of a major scientific paradigm shift from the conventional mathematics to the mathematics of uncertainty, which arguably holds the key to solving very difficult and complex problems in biological and social sciences alike. The book, loaded with historical perspective, creative thinking, critical reviewing, and uniquely constructed strategy for future growth of this dynamic research area, is an invaluable resource for active researchers at all levels, university administrators, foundation directors, funding agency program chiefs, research & development planners and technological assessors
备用描述
The main subject of this book is the fuzzy c -means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c -means is that most methodology and application studies in fuzzy clustering use fuzzy c -means, and hence fuzzy c -means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c -means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c -means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c -means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.
备用描述
<p><P>The foundations of revealed preference theory for a competitive consumer were laid by Samuelson in 1938. Later this theory was axiomatically developed by Arrow, Sen, Suzumura and other economists into the theory of choice functions.<p>This book extends the theory of revealed preference to fuzzy choice functions and provides applications to multicriteria decision making problems. The main topics of revealed preference theory (rationality, revealed preference and congruence axioms, consistency conditions) are treated in the framework of fuzzy choice functions. New topics, such as the degree of dominance and similarity of vague choices, are developed. The results obtained are applied to economic problems where partial information and human subjectivity involve vague choices and vague preferences. The book contains a number of new results achieved by the author. Even though the text is reasonably self-contained, previous knowledge of revealed preference and fuzzy set theory is helpful for the reader. <p>Social choice theorists and computer scientists will find in this monograph stimulating material for further research and concrete applications.</p>
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In 1965 Lotfi Zadeh, a professor of electrical engineering at the University of California in Berkeley, published the first of his papers on his new Fuzzy Set Theory. Since the 1980s this mathematical theory of "unsharp amounts" has been applied in many different fields with great success. The word "fuzzy" has also become very well-known among non-scientists thanks to extensive advertising campaigns for fuzzy-controlled household appliances and to their prominent presence in the media, first in Japan and then in other countries. On the other hand, the story of how Fuzzy Set Theory and its earliest applications originated remains largely unknown. In this book, the history of Fuzzy Set Theory and the ways it was first used are incorporated into the history of 20th century science and technology. Influences from philosophy, system theory and cybernetics stemming from the earliest part of the 20th century are considered alongside those of communication and control theory from mid-century. Today, Fuzzy Set Theory is the core discipline of "soft computing," and provides new impetus for research in the field of Artificial Intelligence
备用描述
<p><p>this Book Brings Together A Collection Of Classic Research Papers On The Dempster-shafer Theory Of Belief Functions. This Book Will Serve As The Authoritative Reference In The Field Of Evidential Reasoning And An Important Archival Reference In A Wide Range Of Areas Including Uncertainty Reasoning In Artificial Intelligence And Decision Making In Economics, Engineering, And Management. The Carefully Selected Contributions Are Grouped Into Seven Sections, Including Conceptual Foundations, Theoretical Perspectives, Theoretical Extensions, Alternative Interpretations, And Applications To Artificial Intelligence, Decision-making, And Statistical Inferences. The Book Also Includes A Foreword By Dempster And Shafer Reflecting The Development Of The Theory In The Last Forty Years, And An Introduction Describing The Basic Elements Of The Theory And How Each Paper Contributes To The Field.</p>
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Front Matter....Pages -
Introduction....Pages 1-7
BasicMethods for c -Means Clustering....Pages 9-42
Variations and Generalizations - I....Pages 43-66
Variations and Generalizations - II....Pages 67-98
Miscellanea....Pages 99-117
Application to Classifier Design....Pages 119-155
Fuzzy Clustering and Probabilistic PCA Model....Pages 157-169
Local Multivariate Analysis Based on Fuzzy Clustering....Pages 171-194
Extended Algorithms for Local Multivariate Analysis....Pages 195-233
Back Matter....Pages -
备用描述
"This book extends the theory of revealed preference to fuzzy choice functions and provides applications to multicriteria decision making problems. Even though the text is reasonably self-contained, previous knowledge of revealed preference and fuzzy set theory is helpful for the reader. Social choice theorists and computer scientists will find in this monograph stimulating material for further research and concrete applications."--Jacket
备用描述
Studies in Fuzziness and Soft Computing
Erscheinungsdatum: 19.06.2007
开源日期
2010-02-18
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