Data Mining, Rough Sets and Granular Computing, Tsau Young Lin; Yiyu Y. Yao; Lotfi A. Zadeh

Автор: Dominik Slezak; JingTao Yao; James F. Peters; Wojc
Название: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
ISBN: 3540286608 ISBN-13(EAN): 9783540286608
Издательство: Springer
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Описание: Contains papers from the proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005, held in Regina, Canada in August/September 2005 in 2 volumes.

Автор: Lech Polkowski; Piotr Artiemjew
Название: Granular Computing in Decision Approximation
ISBN: 3319128795 ISBN-13(EAN): 9783319128795
Издательство: Springer
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Описание: A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules.

Автор: Han Liu; Mihaela Cocea
Название: Granular Computing Based Machine Learning
ISBN: 331970057X ISBN-13(EAN): 9783319700571
Издательство: Springer
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Описание: Introduction, - Traditional Machine Learning, - Semi-supervised Learning through Machine Based Labelling, - Nature Inspired Semi-heuristic Learning, - Fuzzy Classification through Generative Multi-task Learning, - Multi-granularity Semi-random Data Partitioning, - Multi-granularity Rule Learning, - Case Studies, - Con

Автор: Witold Pedrycz; Shyi-Ming Chen
Название: Granular Computing and Decision-Making
ISBN: 3319364901 ISBN-13(EAN): 9783319364902
Издательство: Springer
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Описание:

Granularity Helps Explain Seemingly Irrational Features of Human Decision Making.- A Comprehensive Granular Model for Decision Making with Complex Data.-Granularity in Economic Decision Making: An Interdisciplinary Review.- Decision Makers' Opinions Changing Attitude-Driven Consensus Model under Linguistic Environment and Its Application in Dynamic MAGDM.- Using Computing with Words for Managing Non-Cooperative Behaviors in Large Scale Group Decision Making.- A Type-2 Fuzzy Logic Approach for Multi-Criteria Group Decision Making.- Multi-Criteria Influence Diagrams - A Tool for the Sequential Group Risk Assessment.-Consensus Modeling under Fuzziness - A Dynamic Approach with Random Iterative Steps.- Decision Making - Interactive and Interactive Approaches.- Collaborative Decision Making by Ensemble Rule Based Classification Systems.- A GDM Method Based on Granular Computing for Academic Library Management.- Spatial-taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation.- Group Decision Making in Fuzzy Environment - An Iterative Procedure Based on Group Dynamics.- Fuzzy Optimization in Decision Making of Air Quality Management.- Group Decision Making in Fuzzy Environment - An Iterative Procedure Based on Group Dynamics.- Fuzzy Optimization in Decision Making of Air Quality Management.

Автор: Kacprzyk
Название: Granular, Soft and Fuzzy Approaches for Intelligent Systems
ISBN: 3319403125 ISBN-13(EAN): 9783319403120
Издательство: Springer
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Описание: This book offers a comprehensive report on the state-of-the art in the broadly-intended field of “intelligent systems”. After introducing key theoretical issues, it describes a number of promising models for data and system analysis, decision making, and control. It discusses important theories, including possibility theory, the Dempster-Shafer theory, the theory of approximate reasoning, as well as computing with words, together with novel applications in various areas, such as information aggregation and fusion, linguistic data summarization, participatory learning, systems modeling, and many others. By presenting the methods in their application contexts, the book shows how granular computing, soft computing and fuzzy logic techniques can provide novel, efficient solutions to real-world problems. It is dedicated to Professor Ronald R. Yager for his great scientific and scholarly achievements, and for his long-lasting service to the fuzzy logic, and the artificial and computational intelligence communities. It has been motivated by the authors’ appreciation of his original thinking and groundbreaking ideas, with a special thought to his valuable research on the computerized implementation of various aspects of human cognition for decision-making and problem-solving.

Автор: Dominik Slezak; Marcin Szczuka; Ivo Duentsch; Yiyu
Название: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
ISBN: 3540286535 ISBN-13(EAN): 9783540286530
Издательство: Springer
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Описание: Part of a two volume set, this title includes information related to rough set approximations, rough-algebraic foundations, feature selection and reduction, rough-probabilistic approaches, rough-fuzzy hybridization, fuzzy methods in data analysis, evolutionary computing, machine learning, probabilistic network models, granular computing, and more.

Автор: Hiroshi Sakai; Mihir Chakraborty; Aboul-Ella Hassa
Название: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
ISBN: 3642106455 ISBN-13(EAN): 9783642106453
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Описание: This book constitutes the refereed proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2009, held in Delhi, India in December 2009 in conjunction with the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009.

Автор: Yiyu Yao; Qinghua Hu; Hong Yu; Jerzy W. Grzymala-B
Название: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
ISBN: 331925782X ISBN-13(EAN): 9783319257822
Издательство: Springer
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Цена: 8944.00 р.
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Описание: Cyberspace is a ubiquitous realm interconnecting every aspect of modern society, enabled by broadband networks and wireless signals around us, existing within local area networks in our schools, hospitals and businesses, and within the massive grids that power most countries. Securing cyberspace to ensure the continuation of growing economies and to protect a nations way of life is a major concern for governments around the globe.This book contains papers presented at the NATO Advanced Research Workshop ARW entitled Best Practices and Innovative Approaches to Develop Cyber Security and Resiliency Policy Framework, held in Ohrid,

Автор: Rafael Bello; Rafael Falc?n; Witold Pedrycz
Название: Granular Computing: At the Junction of Rough Sets and Fuzzy Sets
ISBN: 3642095682 ISBN-13(EAN): 9783642095689
Издательство: Springer
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Описание: This volume is a compilation of the best papers presented at the First International Symposium on Fuzzy and Rough Sets (ISFUROS 2006) held in Santa Clara, Cuba.

Автор: Piotr Ho?ko
Название: Granular-Relational Data Mining
ISBN: 3319527509 ISBN-13(EAN): 9783319527505
Издательство: Springer
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Описание: Preface.- Chapter 1: Introduction.- Part I: Generalized Related Set Based Approach.- Chapter 2: Information System for Relational Data.- Chapter 3: Properties of Granular-Relational Data Mining Framework.- Chapter 4: Association Discovery and Classification Rule Mining.- Chapter 5: Rough-Granular Computing.- Part II: Description Language Based Approach.- Chapter 6: Compound Information Systems.- Chapter 7: From Granular-Data Mining Framework to its Relational Version.- Chapter 8: Relation-Based Granules.- Chapter 9: Compound Approximation Spaces.- Conclusions.- References.- Index.

Автор: Lech Polkowski; Piotr Artiemjew
Название: Granular Computing in Decision Approximation
ISBN: 3319366211 ISBN-13(EAN): 9783319366210
Издательство: Springer
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Описание: A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules.

Автор: Sanchez
Название: Type-2 Fuzzy Granular Models
ISBN: 3319412876 ISBN-13(EAN): 9783319412870
Издательство: Springer
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Описание: A series of methods for the formation of higher-type information granules represented by Interval Type-2 Fuzzy Sets are also shown, via multiple approaches, such as Coefficient of Variation, principle of justifiable granularity, uncertainty-based information concept, and numerical evidence based.