Rough Set and Knowledge Technology

Name: Rough Set and Knowledge Technology | 6th International Conference, RSKT 2011, Banff, Canada, October 9-12, 2011, Proceedings
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6th International Conference, RSKT 2011, Banff, Canada, October 9-12, 2011, Proceedings

JingTao Yao Sheela Ramanna Guoyin Wang Zbigniew Suraj(Editor)

Springer (Publisher)

Published on 2. October 2011

XVIII, 771 pages

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978-3-642-24425-4 (ISBN)

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Content

Title
Preface
Organization
Table of Contents
Keynote Papers
Mining Incomplete Data-A Rough Set Approach
Introduction
Rough Set Approaches to Missing Attribute Values
Global Approximations
Local Approximations
Conclusions
References
Uncertainty and Feature Selection in Rough Set Theory
Introduction
Uncertainty in Rough Set Theory
Information Entropy
Information Granulation
Granular Space Distance
Accelerator of Feature Selection
Conclusions and Further Work
References
Towards Designing Human Centric Systems: A New View at System Modeling with Granular Membership Grades
Introduction
Preliminaries
Description-Based Neighbourhoods
Nearness of Sets of Neighbourhoods
Concluding Remarks
References
Sufficiently Near Sets of Neighbourhoods
Introduction
Preliminaries
Description-Based Neighbourhoods
Nearness of Sets of Neighbourhoods
Concluding Remarks
References
Invited Tutorial
History of Set Theory and Its Extensions in the Context of Soft Computing
Attribute Reduction and Feature Selection
Comparison of Classical Dimensionality Reduction Methods with Novel Approach Based on Formal Concept Analysis
Introduction
Dimensionality Reduction Methods
Classical Methods
The Novel Method
Applications
Analyzed Dataset
Analyses Using CATPCA and FA
Analysis Using the Novel Method
Conclusions and Future Work
References
Rule-Based Estimation of Attribute Relevance
Introduction
Related Works
Attribute Relevance and Properties of Rules
Experiments
Experimental Setup
Experimental Results
Conclusions
References
Applications of Approximate Reducts to the Feature Selection Problem
Introduction
Preliminaries
Approximate Reduct-Based Feature Selection Methods
Evaluation of the Reduct-Based Ranking Methods
Conclusions
References
Dependence and Algebraic Structure of Formal Contexts
Introduction
Preliminaries
Dependence between Formal Contexts
Dependence between Formal Contexts
Independence and Attribute Reduction of Formal Contexts
Lattice Structure of Formal Contexts
Conclusions
References
Optimal Sub-Reducts with Test Cost Constraint
Introduction
Preliminaries
The Optimal Sub-Reducts Problem
Exhaustive Algorithms
Experiments
Conclusions and Further Works
References
An Efficient Fuzzy-Rough Attribute Reduction Approach
Introduction
Preliminaries
An Efficient Algorithm to Fuzzy-Rough Feature Selection
Experimental Analysis
Conclusions
References
A Novel Attribute Reduction Approach Based on the Object Oriented Concept Lattice
Introduction
The Context Matrix and Object Oriented Concept Lattice
Attribute Reduction of Formal Contexts
The Attribute Reduction Algorithms
Conclusions
References
Rough-Set-Inspired Feature Subset Selection, Classifier Construction, and Rule Aggregation
Introduction
Rough-Set-Inspired Rule-Based Classifiers
Rough-Set-Inspired Feature Subset Selection
Rough-Set-Inspired Feature Subset Ensembles
Experimental Framework and Results
Conclusions
References
A Constructive Feature Induction Mechanism Founded on Evolutionary Strategies with Fitness Functions Generated on the Basis of Decision Trees
Introduction
The CIDT Algorithm
Discussion
References
An Efficient Fuzzy Rough Approach for Feature Selection
Introduction
Mutual Information-Based Algorithm for Fuzzy Rough Feature Selection (MIFRFS)
Proposed Feature Selection Algorithm
Experiments and Results
Conclusions and Future Work
References
Partitions, Coverings, Reducts and Rule Learning in Rough Set Theory
Introduction
Representations of Concepts and Classification
Pawlak Three-Step Approach to Rule Learning
Step 1: Construction of an Attribute Reduct of a Table with Respect to Classification Attributes
Step 2: Construction of an Attribute Reduct of an Object with Respect to Classification Attributes
Step 3: Construction of a Rule Reduct
Discussions and Variations of Pawlak Approach
Conclusion
References
A Rough Set Approach to Feature Selection Based on Relative Decision Entropy
Introduction
Preliminaries
Feature Selection Based on Relative Decision Entropy
Experiment
Conclusion
References
Generalized Rough Set Models
A Variable Precision Covering Generalized Rough Set Model
Introduction
Preliminaries
Variable Precision Extension for Covering Generalized Rough Set Model
Conclusion
References
Dominance-Based Rough Set Approach on Pairwise Comparison Tables to Decision Involving Multiple Decision Makers
Introduction
The Pairwise Comparison Table (PCT) as Preference Information and as a Learning Sample
Induction of Decision Rules from Rough Approximations of Weak Preference Relations
DRSA for Analysis of a Pairwise Comparison Table in Case of a Plurality of Decision Makers - Definitions
Conclusions
References
Generalized Parameterized Approximations
Introduction
Equivalence Relations
Complete Data
Parameterized Approximations
Arbitrary Binary Relations
Nonparameterized Approximations
Parameterized Approximations
Incomplete Data Sets
Definability
Conclusions
References
Transversal and Function Matroidal Structures of Covering-Based Rough Sets
Introduction
Basic Definitions
Matroidal Structures of Covering-Based Rough Sets
Matroidal Structure by Transversal Theory
Matroidal Structure by the Upper Approximation Number
The Relationship between Proposed Two Matroidal Structures
Conclusions
References
Some Fuzzy Topologies Induced by Rough Fuzzy Sets
Introduction
Rough Fuzzy Approximation Operators
Basic Concepts of Fuzzy Topological Spaces
Fuzzy Topologies of Rough Fuzzy Sets
Conclusion
References
Neighborhood Rough Sets Based Matrix Approach for Calculation of the Approximations
Introduction
Preliminaries
An Algorithm for Calculation of the Approximations in the Neighborhood System
An Illustration
Conclusions
References
Machine Learning with Rough and Hybrid Techniques
Case-Based Classifiers with Fuzzy Rough Sets
Introduction
Review of Fuzzy Rough Sets
A Case-Based Classifier Based on Fuzzy Rough Sets
Case Selection with Fuzzy Rough Sets
Case-Based Classifier Based on Fuzzy Rough Sets
Experimental Results
Conclusions
References
Comparison of Greedy Algorithms for a-Decision Tree Construction
Introduction
Basic Notions
Decision Tables and Trees
Uncertainty Measures
Impurity Functions
Cost Functions
Greedy Approach
Dynamic Programming Approach
Experimental Results
Conclusions
References
Constructing an Optimal Decision Tree for FAST Corner Point Detection
Introduction
Corner Point Detection Problem
Algorithms of Decision Tree Construction
Basic Notions
Greedy Approach
Dynamic Programming Approach
Experiments
Conclusions
References
Incremental Learning in AttributeNets with Dynamic Reduct and IQuickReduct
Introduction
Proposed System
Experiment and Results
Conclusion
References
LEM2-Based Rule Induction from Data Tables with Imprecise Evaluations
Introduction
A Rough Set Approach to Imprecise Decision Tables
Rule Induction from Imprecise Decision Tables
Numerical Experiments
References
An Extension to Rough c-Means Clustering
Introduction
Basic Concepts
Rough Sets
Rough c-Means Algorithm
An Extension to Rough c-Means Algorithm
Motivations
Detailed Descriptions
Experimental Results
Conclusion
References
A Modified Cop-Kmeans Algorithm Based on Sequenced Cannot-Link Set
Introduction
Cop-Kmeans Clustering Algorithm
The Shortcomings of Cop-Kmeans Algorithm
Background
The Proposed CLC-Kmeans Algorithm
Experimental Results
Data Sets
Experimental Methodology
Results
Conclusion
References
A NIS-Apriori Based Rule Generator in Prolog and Its Functionality for Table Data
Introduction
Rule Generation in DISs and NISs
Rule Generation in a DIS
Rule Generation in a NIS
Stability Factor of Rules in the Upper System
Functionality of NIS-Apriori Based Rule Generator
Case 1: Criterion Based Rule Generation in a DIS
Case 2: Consistency Based Rule Generation in a DIS
Case 3: Rule Generation in a DIS with Missing Values
Case 4: Consistency Based Rule Generation in a NIS
Concluding Remarks
References
Towards a Practical Approach to Discover Internal Dependencies in Rule-Based Knowledge Bases
Introduction
Preliminaries and Basic Notation
Decision Units as a Decision Model for Rule Base
Rules Clusters as a Decision Model for Rule Base
Conclusions
References
Discovering Patterns of Collaboration in Rough Set Research: Statistical and Graph-Theoretical Approach
Introduction
Pawlak Collaboration Graph
Basic Analysis of Pawlak Graph
Collaboration and Influence
Cores
Lords
Conclusions
References
Knowledge Technology
Comparing a Clustering Density Criteria of Temporal Patterns of Terms Obtained by Different Feature Sets
Introduction
An Integrated Method for Detecting Remarkable Trends of Technical Terms as Temporal Patterns of Importance Indices
Extracting Technical Terms in a Set of Documents
Calculating Importance Index Values in Each Time-points for Each Term
Features for Obtaining Temporal Clusters
Assigning Meanings for Obtained Temporal Patterns
Extracting Temporal Patterns of Terms by Using The Features based on Linear Regression Analysis
Extracting Technical Terms from Entire Documents
Extracting Temporal Patterns and Evaluation of Clustering Density Criteria
Conclusion
References
Similarity of Query Results in Similarity-Based Databases
Introduction and Motivation
Preliminaries
Structures of Truth Degrees, Fuzzy Sets, and Fuzzy Relations
Ranked Data Tables over Domains with Similarities
Relational Operations for Similarity-Based Queries
Similarity of Ranked Data Tables
References
Rough Set Based Quality of Service Design for Service Provisioning in Clouds
Introduction
Background
System Overview
Architecture of MC-QoSMS Framework
MC-QoSMS Algorithm
Interaction with NRM
Results and Discussion
Conclusions
References
GTrust: A Distributed Trust Model in Multi-Agent Systems Based on Grey System Theory
Introduction
GTrust Model
Rating to Interaction
Trust to Targets
Rate the Witness
Trust to Witnesses
Experiments and Result Analysis
Future Work
References
Linear Necessity Measures and Their Applications to Possibilistic Linear Programming
Introduction
Necessity Measures and Modifier Generating Functions
Linear Necessity Measures
Applications to Possibilistic Linear Programming
Conclusions
References
Remarks on Pairwise Comparison Numerical and Non-numerical Rankings
Introduction
Classical Ranking Based on Pairwise Comparisons
Model
Questionable Assumptions
Pairwise Comparison Based Non-numerical Ranking
Partial Orders
Model
Algorithms
Mutual Relationship
Final Comments
References
Community-Based Relational Markov Networks in Complex Networks
Introduction
Preliminaries
Markov Networks
Relational Markov Networks
Community-Based Relational Markov Networks
Discriminative Maximum Pseudolikelihood Estimation
Experiments
Collective Classification
Link Prediction
Conclusion
References
Intelligent Systems and Applications
Applying Multi-Criteria Decision Analysis to Global Software Development with Scrum Project Planning
Introduction
Decision Setting
Real World Scenario
Planning Global Scrum Projects
Structuring Phase
Defining The Problem
Defining Primary Evaluation Elements
Building Concepts
The Concept Hierachy and MACBETH
Evaluation and Recommendation
Conclusion
References
Accuracy Evaluation of the System of Type 1 Diabetes Prediction
Introduction
Data Description
Classification Problem
Classification Accuracy
Rules Synthesis (Using Reducts)
Rules Synthesis Using LEM2 Algorithm
Conclusions
References
Driver Status Recognition by Neighborhood Covering Rules
Introduction
Algorithm Overview
Feature Estimation with Neighborhood Rough Model
Neighborhood Rough Sets
Feature Selection Algorithm
Rule Learning Based on Relative Covering Reduction
Experimental Analysis
Conclusions
References
Application of Gravitational Search Algorithm on Data Clustering
Introduction
Clustering Problem
Gravitational Search Algorithm
The Proposed Approach
Experimental Results
Conclusion
References
Application of Rough Sets in GIS Generalization
Introduction
GIS Generalization
Application of Rough Sets in GIS Generalization
Method of Adding Fuzzy Decisional Attribute
Method of Objects Dynamic Assessment by Rough Sets Theory
Experiment
Conclusion
References
Application of Rough Set Theory for Evaluating Polysaccharides Extraction
Introduction
Attribute Significance and Attribute Reduction
Analysis of Factors of the Extraction Yield of Polysaccharides
Results of Orthogonal Tests
Construction of a Decision Table
Rough Set Analysis
Conclusions
References
Identification of Print Technology Based on Homogeneous Regions of Image
Introduction
Printing Technologies
Variogram
Feature Analysis of Variogram in Print Technology Identification
Gaussian Variogram Model
RDT Algorithm
GVM Algorithm for Print Technology Identification
GVMPT Algorithm
Influence of Algorithmic Parameters in GVMPT
Experiment Results
References
Ant Based Clustering of MMPI Data - An Experimental Study
Introduction
MMPI Data
The Copernicus System
Dissimilarity Measures for Profiles
Minimum Distance Metrics
Du Mass Index
Pearson Index
Cohen Index
Ant Based Clustering
Experiments
Conclusions
References
Detection of Cancer Patients Using an Innovative Method for Learning at Imbalanced Datasets
Introduction
Proposed Method
Experimental Results
Conclusion
References
Information Reuse in Hospital Information Systems: A Similarity-Oriented Data Mining Approach
Introduction
Hospital Information System: Cyberspace in Hospital
Basic Unit in HIS: Order
Data Preparation and Analysis
Visualizing Hospital Actions from Data
Clustering of Temporal Trends of Orders
Conclusions
References
A Model-Based Decision Support Tool Using Fuzzy Optimization for Climate Change
Introduction
The Integrated FGP Model and FAHP for RSD
Fuzzy Analytic Hierarchy Process (FAHP)
FGP Model Formulation
Solution Methodology
Uncertainty Handling
Goals Priority and Differential Weights Setting
Proposed FGP-FAHP Methodology
Conclusions and Future Works
References
Clustering of Rough Set Related Documents with Use of Knowledge from DBpedia
Introduction
The Purpose and Methodology of the Study
Data Acquisition and Preparation
The DBpedia Knowledge Base
Experimental Evaluation of the Approach
Conclusions and Further Work
References
Case-Based Reasoning Using Dominance-Based Decision Rules
Introduction
Dominance-Based Rough Approximation for Case-Based Reasoning
Induction and Application of Decision Rules
Illustrative Example
Conclusions
References
RoSetOn: The Open Project for Ontology of Rough Sets and Related Fields
Introduction
Domain Ontology
The Outline of Intelligent Searching Using the Defined Ontology
Exemplary Application
Conclusions
References
Fuzzy Description of Air Quality: A Case Study
Introduction
Fuzzy Description of Air Quality
Convex Normalized Fuzzy Number
Matching Between Fuzzy Values
Type 1 Fuzzy Inference System
Case Study
Results and Discussion
Concluding Remarks
Future Scope for Research
References
A Robust Face Recognition Method Based on AdaBoost, EHMM and Sample Perturbation
Introduction
Face Recognition Based on AdaBoost and EHMM
Face Recognition Based on EHMM
Face Recognition Model Based on AdaBoost and EHMM
Sample Perturbation
Experiments and Discussion
Experimental Condition
Experiments and Results
Conclusion
References
Roughness Approach to Color Image Segmentation through Smoothing Local Difference
Introduction
Quantitative Roughness Measurement
Representing Local Consistency
Roughness Measure of Color Distribution
Experimentation and Validation
Conclusion
References
On Local Inclusion Degree of Intuitionistic Fuzzy Sets
Introduction
Preliminaries
IF Local Inclusion Degree
Similarity Characterization Based on the Local Inclusion Degree
Conclusion
References
Special Session: Decision-Theoretic Rough Set Model
Analysis of Data-Driven Parameters in Game-Theoretic Rough Sets
Introduction
Game-Theoretic Rough Sets
Decision-Theoretic Rough Sets
Game-Theoretic Rough Sets
Analysis of Parameters
GTRS Analysis Software
Analysis of and Parameters
Analysis of Approximation Measures
Conclusions
References
An Optimization Viewpoint of Decision-Theoretic Rough Set Model
Introduction
Basic Notions of Decision-theoretic Rough Set Model
An Optimization Problem in Decision-Theoretic Rough Set Model
Learning Thresholds and Cost Functions from Data
Attribute Reduction Based on Minimal Decision Cost
Generality of the Optimization Problem
Conclusion
References
Attribute Reduction in Decision-Theoretic Rough Set Model: A Further Investigation
Introduction
Preliminaries
Attribute Reduction in DTRS Model
Monotonicity of Positive Region in PRS
Monotonicity of Positive Region in DTRS
Attribute Reduction in DTRS
Experimental Analysis
Conclusion
References
A New Discriminant Analysis Approach under Decision-Theoretic Rough Sets
Introduction
Preliminaries
Discriminant Analysis and Binary Logistic Regression
Decision-Theoretic Rough Sets
The New Discriminant Analysis Approach under Decision-theoretic Rough Sets
An Illustration
Conclusions
References
Construction of a-Decision Trees for Tables with Many-Valued Decisions
Introduction
Main Notions
Auxiliary Statements
Algorithm U for -Decision Tree Construction
Problem of Recognition of Colors of Points in the Plain
Conclusions
References
Decision Making in Incomplete Information System Based on Decision-Theoretic Rough Sets
Introduction
Incomplete Information Table and Its Extensions
Probability of an Object Belonging to a Concept
An Example of Using the New Method
Conclusion
References
Automatically Determining the Number of Clusters Using Decision-Theoretic Rough Set
Introduction
Basic Theory
Agglomerative Hierarchical Clustering Algorithm
Decision-Theoretic Rough Set Model
Automatically Determining the Number of Clusters
Clustering Validity Evaluation Based on DTRS
Automatic Clustering Algorithm Based on DTRS
Validation of the Algorithm
Experimental Results
Conclusion
References
A New Formulation of Multi-category Decision-Theoretic Rough Sets
Introduction
Three-Way Decisions with DTRS
Multi-category Classification with DTRS
Existing Work
A New Formulation
An Example
Conclusions and Future Work
References
Special Session: Near Sets
Parallel Computation in Finding Near Neighbourhoods
Introduction
Background on Near Sets and GPU CUDA
Near Sets
CUDA GPU Programming
GPU Algorithm
Finding Neighbourhoods
Finding Tolerance Classes
Complexity
CBIR Application and Results
Conclusion
References
e-Near Collections
Introduction
Preliminaries
e-Approach Nearness of Collections
Intuitive Notion of Nearness
Nearness of Collections of Neighbourhoods
Sample Image Analysis Implementation of Merotopies
Concluding Remarks
References
Nearness of Subtly Different Digital Images
Introduction
Preliminaries: Anisotropic Wavelets and Tolerance Nearness Measure
Anisotropic Wavelet-Based Tolerance Nearness
Image Comparison Methodology
Experimental Results
Conclusion
References
A Generalization of Near Set Model
Introduction
Preliminaries of the AFS Algebra
Generalized Near Sets Based on AFS Theory
Near Sets with Multi-granulation Probe Function
Near Sets Based AFS Fuzzy Description
Generalized Approximation Space Based on AFS Fuzzy Description Logics
Conclusion
References
Gauges, Pregauges and Completions: Some Theoretical Aspects of Near and Rough Set Approaches to Data
Introduction
Mathematical Preliminaries
Near Set Theory
Rough Set Theory
Gauge Spaces
Pregauges and Completions in the Near Set Framework
Conclusions
References
Special Session: Quotient Space Theory
Path Queries on Massive Graphs Based on Multi-granular Graph Partitioning
Introduction
Quotient Space Theory
Multi-granular Graph Partitioning
Multi-granular Graph Partitioning
Path Queries on Massive Graphs
Experimental Results
Conclusion
References
A Minimal Test Suite Generation Method Based on Quotient Space Theory
Introduction
The Granular Analysis Method of Quotient Space
Test Suite Model of Quotient Space
The Minimal Test Suite Generation Algorithm
An Example
Conclusions
References
Audio Signal Blind Deconvolution Based on the Quotient Space Hierarchical Theory
Introduction
Crucial Principles in Quotient Space Theory
Convolutive Mixing Model
Quotient Space Theory of Time-frequency Domain Blind Deconvolution Algorithm
Simulation Results
Conclusions
References
The Optimal Approximation of Fuzzy Tolerance Relation
Introduction
Terminology
Some Definitions
Combination and Decomposition
Several Different Sets of Partitions Approximation
The Properties of Approximation
Basic Properties
Algorithm
The Optimal Approximation of Fuzzy Tolerance Relation
Conclusion
References
Special Session: Rough Sets in Process Mining
A New Method for Inconsistent Multicriteria Classification
Introduction
Multicriteria Classification by VC-DRSA
New Method for Inconsistent Multicriteria Classification
An Uncertainty Measure
TIPStoC Algorithm
Illustrative Example and Experiments
Illustrative Example
Experiments
Conclusions
References
Probabilistic Similarity-Based Reduct
Introduction
Assumptions
A New Type of Reduct
Probabilistic Similarity-Based Classification
Testing the Subsets of Attributes
The Formulation of Reduct
An Illustrative Example
Relation to the Existing Approaches
Final Remarks
References
Inference Processes in Decision Support Systems with Incomplete Knowledge
Introduction
Decision Support Systems
The Solution
Cluster Analysis
Agnes
Relation to Decision Support Systems
TheExperiments
The Test Cases
Choosing the Cluster Representative
Choosing the Distance Measure
Cluster Joining Criterion
The Conclusions
References
Synthesis of Synchronized Concurrent Systems Specified by Information Systems
Introduction
Shared Update of Memory Problem
The Synthesis Problem and Proposed Solution
Conclusions
References
Efficiency of Complex Data Clustering
Introduction
Role of Domain Knowledge in Solving Data Mining Problems
Structure of the Dataset
The Clustering Algorithm, Choosing the Representatives
The Executed Experiments
Summary
References
Workshop: Advances in Granular Computing 2011
The Extraction Method of DNA Microarray Features Based on Experimental A Statistics
Introduction
Weighted Voting Classifier - 8_v1.4 Algorithm
Feature Extraction Method Based on A Statistics - SAM5
The Results of Experiments for Exemplary DNA Microarray Data
The Results of SAM5 Gene Extraction Method
Conclusion
References
Granular Structures in Graphs
Introduction
Purpose of the Study
Related Studies
Granules and Granular Structures in Graphs
Granules and Granular Structures
Granules in a Graph
Granular Structures in a Graph
Extraction of the Granular Structures in a Graph
Degree-Based Model
Weight-Based Model
Conclusion
References
Fuzzy Rough Granular Self Organizing Map
Introduction
Proposed Method
Input Vector Representation in Terms of Fuzzy Granules
Granulations and Approximations on the Structures Using Fuzzy Rough Sets
Incorporation of Fuzzy Rough Sets in SOM
Experimental Results
Conclusion
References
Knowledge Acquisition in Inconsistent Multi-scale Decision Systems
Introduction
BasicNotions
Pawlak Rough Sets
Decision Systems
Decision Rules
Multi-scale Decision Systems
Knowledge Acquisition
Conclusion
References
Text Clustering Based on Granular Computing and Wikipedia
Introduction
Text Granular Representation
Text Clustering via Granular Computing
Granule Weighting
Clustering Procedure
Experimental Results
Dataset
Document Representation
Clustering Results and Discussion
Conclusion and Future Work
References
Rough Relations, Neighborhood Relations, and Granular Computing
Introduction
Rough Relations
Exponent Poset
Indistinguishability Relations
Rough Relations
An Example
Neighborhood Relations
Rough Relations under Neighborhood Relations
Granular Computing Using Rough Relations
Conclusion
References
Comparing Clustering Schemes at Two Levels of Granularity for Mobile Call Mining
Introduction
Mobile Phone Call Data Mining
Data and Experimental Design
Proposed Granular Transformation of Clustering Schemes
Results and Discussions
Conclusions
References
Granular-Based Partial Periodic Pattern Discovery over Time Series Data
Introduction
Granular-Based Partial Periodic Pattern Discovery
Algorithm Description
Complexity Analysis
Noise and Redundancy Treatment
Experiments
Artificial Data Set
Real data set
Conclusion
References
Approximations of Functions: Toward Rough Granular Calculus
Introduction
Approximation Spaces
Function Approximations
Conclusions
References
Bipartite Graphs and Coverings
Introduction
Basic Definitions
Bipartite Graphs and Coverings
Conclusions
References
Covering-Based Reduction of Object-Oriented Concept Lattices
Introduction
Preliminaries
Reduction of Object-oriented Concept Lattices
Reduction Based on Covering of Attribute Set
Reduction Based on Covering of Object Set
Examples
Type I Compression Method
Type II Compression Method
Conclusion
References
Top-Down Progressive Computing
Introduction
A Model of Top-Down Progressive Computing
Multilevel Granular Structures
A Basic Progressive Computing Algorithm
Examples of Progressive Computing
Progressive Computing as a Methodology of Problem Solving for Humans
Progressive Computing as a Paradigm of Information Processing for Machines
Conclusion
References
Least Absolute Deviation Cut
Introduction
Minimum Cut and Its Generalizations
Least Absolute Deviation Cut
LAD Cut Assumption about Similarity Matrix
Numerical Experiments
Artificial Data Sets
Image Segmentation
Discussions and Conclusions
References
Hierarchical Qualitative Inference Model with Substructures
Introduction
Preliminaries
Hierarchical Qualitative Inference Model
Mesoscale Substructures Inference with Edge-Deletion
Metastructures Inference with Basic Decomposition
An Inference Case
Conclusions
References
Decision Rules for Decision Tables with Many-Valued Decisions
Introduction
Main Notions
Set Cover Problem
Exact Covers
Partial Covers
Decision Rules
Exact Decision Rules
Partial Decision Rules
Conclusions
References
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Rough Set and Knowledge Technology

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