Fuzzy-Set Social Science

Book Description

In this innovative approach to the practice of social science, Charles Ragin explores the use of fuzzy sets to bridge the divide between quantitative and qualitative methods. Paradoxically, the fuzzy set is a powerful tool because it replaces an unwieldy, "fuzzy" instrument—the variable, which establishes only the positions of cases relative to each other, with a precise one—degree of membership in a well-defined set.

Ragin argues that fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible. They let quantitative researchers abandon "homogenizing assumptions" about cases and causes, they extend diversity-oriented research strategies, and they provide a powerful connection between theory and data analysis. Most important, fuzzy sets can be carefully tailored to fit evolving theoretical concepts, sharpening quantitative tools with in-depth knowledge gained through qualitative, case-oriented inquiry. This book will revolutionize research methods not only in sociology, political science, and anthropology but in any field of inquiry dealing with complex patterns of causation.

Customer Reviews:

This book is a great first step in reorienting social science.......2007-07-13

Where to start? Well, first the second reviewer is off the mark with the critique about the specificity regarding the IMF. This is not a book about the IMF, so the lack of exhaustive it not a fatal flaw. What this book attempt to accomplish is to get one to think about new ways of categorizing social phenomena. Too much of quantitative method assumes that one can make clear distinctions between categories. This is stating the case much too strongly to reflect reality in an acceptable manner. What Ragin does is offer some theorizing about how one can recognize the fuzzy-boundaries of social phenomena. His insight rests on the use of key characteristics to provide the metric for those who will try to better reflect social events in their research. If you wish to create a better explanation of what you are studying then purchase this book. It is sure to improve your research and how you theorize about the objects you study.

Fuzzy subjectivity.......2005-05-05

I find crisp sets problematic. Hoping to resolve some of the problems (for qualitative and comparative research) of crisp sets, I turned to this text. Ragin repeats himself time and time again in this text. Some complex topics (concentration, dilation, complex theoretical statements) receive minimal attention, leaving readers out in the cold. He fails to establish the efficacy of fuzzy sets for sufficiency or necessity. Ragin also fails to support why necessity is either valuable or possible in social sciences. I would seek to argue that necessity is virtually impossible to determine in social sciences. J.S. Mill was no sociologist. Also, when Ragin attempts to illustrate the application of fuzzy-set methods to IMF protests (chapter 10), he seems to neglect to include certain steps in the calculation of membership scores in the set with severe IMF protests. Ragin does not provide enough qualitative data to support why each country was indicated to have membership in his seven levels of IMF protest membership (p. 265). Also, when Ragin discusses assessment of fuzzy set membership (p. 165 - 171), I feel that fuzzy set membership assignment provides too much flexibility to individual researchers that severely hampers retestability (or even critical analysis) of the data. Ragin skirts the issue of researcher subjectivity when he makes statements like (p. 166, and similar statements in other portions of the text): "The steps I sketch here assume that researchers have a solid understanding of the concepts appearing in their theories and that they have an extensive base of relevant substantive knowledge as well." I certainly feel that the 'crisp-set approach' can be highly problematic, but Ragin's fuzzy-set approach does not seem to be a well-formulated answer to social scientific quandrys presented by crisp sets.

useful place to start but by no means the final story.......2001-10-25

There are two aspects to Ragin's book. The first part of the book is really an update of his general methodological program first set forth in The Comparative Method. It does add some new things to the older material and is thus worth the look. Ragin has some important points to make and deserves to be paid careful attention by social scientists of both qualitative and quantitative traditions.

The second part is a relatively gentle--too gentle in my view--introduction to fuzzy set theory as well as to some applications to social scientific problems that can be addressed with FST. This is useful since it won't put readers off with lots of heavy math they're unlikely to understand. However, the presentation of FST in this book is weak. IMO the worst deficiency is that it lacks cites to more detailed literature that would be necessary for anyone who wanted to apply FST to real problems.

Book Description

This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.

Customer Reviews:

nice introduction to topic for computer science and stats.......2001-05-06

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

Broadly Useful Reference For Intellignet Data Analysis.......2000-03-06

This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.

The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.

Book Description

This innovative volume explores graphical models using belief functions as a representation of uncertainty, offering an alternative approach to problems where probability proves inadequate. Graphical Belief Modeling makes it easy to compare the two approaches while evaluating their relative strengths and limitations. The author examines both theory and computation, incorporating practical notes from the author's own experience with the BELIEF software package. As one of the first volumes to apply the Dempster-Shafer belief functions to a practical model, a substantial portion of the book is devoted to a single example--calculating the reliability of a complex system. This special feature enables readers to gain a thorough understanding of the application of this methodology. The first section provides a description of graphical belief models and probablistic graphical models that form an important subset: the second section discusses the algorithm used in the manipulation of graphical models: the final segment of the book offers a complete description of the risk assessment example, as well as the methodology used to describe it. Graphical Belief Modeling offers researchers and graduate students in artificial intelligence and statistics more than just a new approach to an old reliability task: it provides them with an invaluable illustration of the process of graphical belief modeling.

Customer Reviews:

goes beyond the idea of a probability distribution .......2006-05-15

A belief function is a step beyond traditional probability distribution functions. The latter describe uncertainty. But, by definition, you somehow know precisely that a given pdf is a correct description of a process. A belief function tries to express the imprecision in knowledge about a pdf.

The book is an advanced treatment of how graphical methods can be used to aid in the construction of belief models. Be aware though that the book has complicated ideas drawn from set theory and graph theory. With rigourous derivations of theorems.

Usages are also stressed. Examples are given of how to model failure rates in systems of many parts. Where a traditional probabilistic analysis may be far too difficult or labourious.

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Component and Correspondence Analysis
Jan L. A. Van Rijckevorsel
Manufacturer: John Wiley & Sons
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Binding: Paperback

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ASIN: 0471918474

Fuzzy Set Theory: Applications in the Social Sciences (Quantitative Applications in the Social Sciences)

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Fuzzy Set Theory: Applications in the Social Sciences (Quantitative Applications in the Social Sciences)
Michael J. Smithson , and Jay Verkuilen
Manufacturer: Sage Publications, Inc
ProductGroup: Book
Binding: Paperback

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ASIN: 076192986X

Book Description

Fuzzy set theory deals with sets or categories whose boundaries are blurry or, in other words, "fuzzy." This book presents an accessible introduction to fuzzy set theory, focusing on its applicability to the social sciences. Unlike most books on this topic, Fuzzy Set Theory: Applications in the Social Sciences provides a systematic, yet practical guide for researchers wishing to combine fuzzy set theory with standard statistical techniques and model-testing.

Key Features:

Addresses Basic Concepts: Fuzzy set theory is an analytic framework for handling concepts that are simultaneously categorical and dimensional. Starting with a rationale for fuzzy sets, this book introduces readers with an elementary knowledge of statistics to the necessary concepts and techniques of fuzzy set theory and fuzzy logic.
Introduces Novel Ways of Analyses: Researchers are shown alternative methods to conventional models, especially for testing theories that are expressed in set-wise terms. Issues of operationalizing graded membership in a fuzzy set and the measurement of the properties of such sets are a few of the topics addressed.
Illustrates Techniques and Applications: Real examples and data-sets from various disciplines in the social sciences are used to demonstrate the connections between fuzzy sets and other data analytic techniques, empirical applications of the technique, and the critiques of fuzzy set theory.

Intended Audience:
Ideal for researchers in the social sciences, education, and behavioral sciences; as well as graduate students in the applied social sciences

Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition

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Excellent advanced book in fuzzy cluster analysis
Worst book I have ever read
Too many unnecessary definitions

Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition
Frank Höppner , Rudolf Kruse , Frank Klawonn , and Thomas Runkler
Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471988642

Book Description

Provides a timely and important introduction to fuzzy cluster analysis, its methods and areas of application, systematically describing different fuzzy clustering techniques so the user may choose methods appropriate for his problem. It provides a very thorough overview of the subject and covers classification, image recognition, data analysis and rule generation. The application examples are highly relevant and illustrative, and the use of the techniques are justified and well thought-out.

Features include:

* Sections on inducing fuzzy if-then rules by fuzzy clustering and non-alternating optimization fuzzy clustering algorithms

* Discussion of solid fuzzy clustering techniques like the fuzzy c-means, the Gustafson-Kessel and the Gath-and-Geva algorithm for classification problems

* Focus on linear and shell clustering techniques used for detecting contours in image analysis

* Accompanying software and data sets pertaining to the examples presented, enabling the reader to learn through experimentation

* Examination of the difficulties involved in evaluating the results of fuzzy cluster analysis and of determining the number of clusters with analysis of global and local validity measures

This is one of the most comprehensive books on fuzzy clustering and will be welcomed by computer scientists, engineers and mathematicians in industry and research who are concerned with different methods, data analysis, pattern recognition or image processing. It will also give graduate students in computer science, mathematics or statistics a valuable overview.

Customer Reviews:

Excellent advanced book in fuzzy cluster analysis.......2004-05-11

Previous reviews are correct, this is not a book for beginners. This is an advanced text for specialists in the area of fuzzy clustering, and as such it is an excellent work. Standard fuzzy c-means, Gustafson-Kessel c-means and Gath-Geva c-means are drawn together in a single modeling framework for near-arbitrary cluster shapes. The cluster validity problem is examined in depth, and experimental results in image analysis are used to illustrate the theoretical material. An excellent monograph.

Worst book I have ever read.......2004-03-24

This book is ridiculous. It has too many unnecessary and, even worse, unclear definitions. It never gives us the meanings of the strange symbols in those absurd definitions. If you want to understand fuzzy cluster analysis in a relatively short time and do not want to suffer, please do not buy this book.

Too many unnecessary definitions.......2004-03-24

This is not a good book for people who want to understand the basic idea of fuzzy cluster analysis in a short time.

Mathematics of Uncertainty: Ideas, Methods, Application Problems (Studies in Fuzziness and Soft Computing)

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Mathematics of Uncertainty: Ideas, Methods, Application Problems (Studies in Fuzziness and Soft Computing)
Hans Bandemer
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540284575

Book Description

"Mathematics of Uncertainty" provides the basic ideas and foundations of uncertainty, covering the fields of mathematics in which uncertainty, variability, imprecision and fuzziness of data are of importance. This introductory book describes the basic ideas of the mathematical fields of uncertainty from simple interpolation to wavelets, from error propagation to fuzzy sets and neural networks. The book presents the treatment of problems of interpolation and approximation, as well as observation fuzziness which can essentially influence the preciseness and reliability of statements on functional relationships. The notions of randomness and probability are examined as a model for the variability of observation and measurement results. Besides these basic ideas the book also presents methods of qualitative data analysis such as cluster analysis and classification, and of evaluation of functional relationships such as regression analysis and quantitative fuzzy data analysis.

Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Fuzzy Value Theory (Studies in Fuzziness and Soft Computing)

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Cost-Benefit Analysis and the Theory of Fuzzy Decisions: Fuzzy Value Theory (Studies in Fuzziness and Soft Computing)
Kofi Kissi Dompere
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540221611

Book Description

This monograph is devoted to the development of value theory of computable general prices in cost-benefit analysis under fuzzy rationality. The book demonstrates the use of fuzzy decision algorithms and logic to develop a comprehensive and multidisciplinary cost-benefit analysis by taking advantage of current scientific gains in fuzziness and soft computing. The theory is developed at a basic level of microeconomics leading to aggregation in fuzzy decision environment composed of subjective phenomenon, imprecision, approximations and problems of information elicitation. The book integrates the theories of computable prices, fuzzy decisions and social decision-choice processes into a set of decision algorithms accessible to practitioners, researchers, graduate students, and policy makers in economics, engineering, as well as physical, social, and medical sciences.

Limit Theorems and Applications of Set-Valued and Fuzzy Set-Valued Random Variables (Theory and Decision Library B)

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Limit Theorems and Applications of Set-Valued and Fuzzy Set-Valued Random Variables (Theory and Decision Library B)
Shoumei Li , Y. Ogura , and V. Kreinovich
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 1402009186

Book Description

This book presents a clear, systematic treatment of convergence theorems of set-valued random variables (random sets) and fuzzy set-valued random variables (random fuzzy sets). Topics such as strong laws of large numbers and central limit theorems, including new results in connection with the theory of empirical processes are covered. The author's own recent developments on martingale convergence theorems and their applications to data processing are also included. The mathematical foundations along with a clear explanation such as Hölmander's embedding theorem, notions of various convergence of sets and fuzzy sets, Aumann integrals, conditional expectations, selection theorems, measurability and integrability arguments for both set-valued and fuzzy set-valued random variables and newly obtained optimizations techniques based on invariant properties are also given.

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