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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

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Cover of 'Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing'

Table of Contents

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    Book Overview
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    Chapter 1 30 Years of Rough Sets and Future Perspectives
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    Chapter 2 Multi-granular Computing in Web Age
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    Chapter 3 Representations for Large-Scale Sequence Data Mining: A Tale of Two Vector Space Models
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    Chapter 4 Individual Approximate Clusters: Methods, Properties, Applications
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    Chapter 5 Some New Progress in Analyzing and Mining Uncertain and Probabilistic Data for Big Data Analytics
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    Chapter 6 Three Approaches to Deal with Inconsistent Decision Tables - Comparison of Decision Tree Complexity
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    Chapter 7 Rough Set-Based Information Dilution by Non-deterministic Information
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    Chapter 8 Belief Discernibility Matrix and Function for Incremental or Large Data
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    Chapter 9 An Experimental Comparison of Three Interpretations of Missing Attribute Values Using Probabilistic Approximations
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    Chapter 10 Efficient Algorithms for Attribute Reduction on Set-Valued Decision Tables
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    Chapter 11 Metric Based Attribute Reduction in Incomplete Decision Tables
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    Chapter 12 The Completion Algorithm in Multiple Decision Tables Based on Rough Sets
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    Chapter 13 Multi-label Classification Using Rough Sets
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    Chapter 14 Enhancing Rough Clustering with Outlier Detection Based on Evidential Clustering
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    Chapter 15 On Dual Intuitionistic Fuzzy Rough Approximation Operators Determined by an Intuitionistic Fuzzy Implicator
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    Chapter 16 Discernibility Matrix Based Attribute Reduction in Intuitionistic Fuzzy Decision Systems
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    Chapter 17 A Fuzzy Rough Set Approach for Incrementally Updating Approximations in Hybrid Information Systems
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    Chapter 18 Implicator-Conjunctor Based Models of Fuzzy Rough Sets: Definitions and Properties
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    Chapter 19 OWA-FRPS: A Prototype Selection Method Based on Ordered Weighted Average Fuzzy Rough Set Theory
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    Chapter 20 Applications of IF Rough Relational Model to Deal with Diabetic Patients
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    Chapter 21 Matrix Representation of Parameterised Fuzzy Petri Nets
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    Chapter 22 A Mathematical Theory of Fuzzy Numbers
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    Chapter 23 Network Performance Analysis Based on Quotient Space Theory
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    Chapter 24 Comparative Study between Extension of Covering Approximation Space and Its Induction through Transversal Matroid
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    Chapter 25 Multi-covering Based Rough Set Model
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    Chapter 26 Boolean Covering Approximation Space and Its Reduction
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    Chapter 27 Dynamic Analysis of IVFSs Based on Granularity Computing
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    Chapter 28 A Novel MGDM Method Based on Information Granularity under Linguistic Setting
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    Chapter 29 The Impacting Analysis on Multiple Species Competition
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    Chapter 30 Topological Characterizations for Three Covering Approximation Operators
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    Chapter 31 Rough Set Granularity: Scott Systems Approach
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    Chapter 32 Incremental Possibilistic K-Modes
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    Chapter 33 Improving Semantic Clustering of EWID Reports by Using Heterogeneous Data Types
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    Chapter 34 Rough Clustering Generated by Correlation Clustering
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    Chapter 35 Recursive Profiles of Businesses and Reviewers on Yelp.com
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    Chapter 36 An Illustrative Comparison of Rough k-Means to Classical Clustering Approaches
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    Chapter 37 A Kidnapping Detection Scheme Using Frame-Based Classification for Intelligent Video Surveillance
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    Chapter 38 Global Decisions Taking on the Basis of Dispersed Medical Data
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    Chapter 39 Soft Clustering to Determine Ambiguous Regions during Medical Images Segmentation
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    Chapter 40 Domain Adaptation for Pathologic Oscillations
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    Chapter 41 Discernibility in the Analysis of Binary Card Sort Data
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    Chapter 42 An Unsupervised Deep-Learning Architecture That Can Reconstruct Paired Images
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Title
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Published by
Springer Berlin Heidelberg, October 2013
DOI 10.1007/978-3-642-41218-9
ISBNs
978-3-64-241217-2, 978-3-64-241218-9
Editors

Ciucci, Davide, Inuiguchi, Masahiro, Yao, Yiyu, Ślęzak, Dominik, Wang, Guoyin

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 50%
Unknown 1 50%
Readers by discipline Count As %
Unknown 2 100%

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