Transactions on Rough Sets XIV

Name: Transactions on Rough Sets XIV
Brand: Springer
Price: 53.49 EUR
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Hiroshi Sakai Mihir Chakraborty Dominik Slezak Aboul-Ella Hassanien William Zhu(Herausgeber*in)

Springer (Verlag)

Erschienen am 27. Juni 2011

VII, 235 Seiten

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978-3-642-21563-6 (ISBN)

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Inhalt

6600
Preface
Table of Contents
Evaluating a Temporal Pattern Detection Method for Finding Research Keys in Bibliographical Data
Introduction
Related Work
An Integrated Method for Detecting Remarkable Trends of Technical Terms as Temporal Patterns of Importance Indices
Extracting Technical Terms in a Given Corpus
Calculating Importance Indices of Text Mining in Each Period
Extracting Temporal Patterns of Technical Terms Based on Importance Indices
Assigning Meanings of the Terms and the Temporal Patterns Based on the Linear Trend to the Timeline
Experiment: Extracting Temporal Patterns of Technical Terms by Using Temporal Clustering
Extracting Technical Terms
Extracting Temporal Patterns by Using k-Means Clustering
Assigning Meanings of the Temporal Patterns Based on the Linear Trends
Details of a Temporal Pattern of the Key Technical Terms
Discussion
Conclusion
References
High Scent Web Page Recommendations Using Fuzzy Rough Set Attribute Reduction
Introduction
Related Work
Information Scent
Introduction
Proposed Information Scent Metric
Modeling the Information Need Using Information Scent
Fuzzy Rough Approach for Attribute Reduction
Introduction to Rough Sets
Rough Set Attribute Reduction
Fuzzy Rough Set Attribute Reduction (FRSAR)
Use of FRSAR in Information Retrieval
Clustering a Query Session
Fuzzy Rough Set Attribute Reduction of Keyword Vector
Experimental Study
Conclusion
References
A Formal Concept Analysis Approach to Rough Data Tables
Introduction
Formal Concept Analysis
Partial Formal Contexts
Partial Contexts Obtained from Streams
The Contest Data Set
Soft Granulation
Conclusion
References
Rough Multiset and Its Multiset Topology
Introduction
Preliminaries and Basic Definitions
Multiset Topologies
M-Basis and Sub M-Basis
M-Topology of mset Relation
Rough Multisets and Its M-Topology
Conclusion and Future Work
References
A Rough Set Exploration of Facial Similarity Judgements
Introduction
Sorting Study of Facial Photographs
Analysis of Pile Composition
Preprocessing of Pair Data
Rough Set Attribute Reduction Methodology
Comparison
Validation
Small Perturbations
Intersection of Reduct Pair Similarities
Discrimination
QU Threshold
Conclusions and Future Work
References
Evolutionary Tolerance-Based Gene Selection in Gene Expression Data
Introduction
Preliminaries
Rough Set Theory
Microarray and Gene Expression Data
T-test
Gene Selection Algorithm Based on Rough Set Theory
Gene Selection Algorithm Based on Tolerance Rough Set Theory
Similarity Measures
Tolerance Rough Set Theory-Based Gene Selection Method
Tolerance-Based Gene Selection via an Evaluation Measure within the Boundary Region
Distance Metric
Gene Selection Based on Distance Function-Assisted Tolerance Rough Set Theory
A Simple Example
Experiments
Conclusions
References
New Approach in Defining Rough Approximations
Introduction
Preliminaries
Some Algebraic Structures of Rough Approximations
New Approach in Defining the Rough Approximations
Conclusion
References
Tolerance Rough Set Theory Based Data Summarization for Clustering Large Datasets
Introduction
Background of Our Work
The Proposed Method
Speeding Up Leader Clustering Method
Summarization Scheme
Clustering Method
Experimental Results
Performance of Speedy Leaders
Application of Single-Link Method to Data Bubble
Performance of R-SL Clustering Method
Conclusions
References
Projected Gustafson-Kessel Clustering Algorithm and Its Convergence
Introduction
Related Work
Projected Gustafson-Kessel Clustering
Projected Gustafson-Kessel(PGK) Clustering Algorithm
Rough Fuzzy Projected Clustering
A Convergence Theorem for the PGK Clustering Algorithm
Experimental Results
Conclusion and Future Work
References
Generalized Rough Sets and Implication Lattices
Introduction
Various Types of Lower and Upper Approximations
Covering-Based Operators
Relation-Based Operators
Partial Ordering of Inclusion Relations and Implication Lattices
Concluding Remarks
References
Classification with Dynamic Reducts and Belief Functions
Introduction
Rough Sets (RS), Generalization Distribution Table (GDT) and the Hybrid System GDT-RS
Rough Sets (RS)
Generalization Distribution Table (GDT)
Generalization Distribution Table and Rough Set System (GDT-RS)
Belief Function Theory
Definitions
Combination
Discounting
Decision Making
Dynamic Belief Rough Set Classifier (D-BRSC) and Dynamic Belief Rough Set Classifier Based on Generalization Distribution Table (D-BRSC-GDT)
Basic Concepts of Rough Sets under Uncertainty
Dynamic Belief Rough Set Classifier (D-BRSC)
Dynamic Belief Rough Set Classifier Based on Generalization Distribution Table (D-BRSC-GDT)
Computational Complexity
Experimentations
Constructing Uncertainty in Databases
Evaluation Criteria and Technique of Sampling
Experimental Results
Conclusion and Future Work
References
Author Index

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