Transactions on Rough Sets XIV
Published on 27. June 2011
VII, 235 pages
978-3-642-21563-6 (ISBN)
Description
The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets
and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence.
Volume XIV contains 11 revised extended papers from the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009, held in Delhi, India. The topics include various rough set generalizations in combination with formal concept analysis, lattice theory, fuzzy sets and belief functions, rough and fuzzy clustering techniques, as well as applications to gene selection, web page recommendation systems, facial recognition, and temporal pattern detection. in addition, this volume contains a regular article on rough multiset and its multiset topology.
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Hiroshi Sakai | Mihir Chakraborty | Dominik Slezak
Transactions on Rough Sets XIV
Book
07/2011
Springer
€53.49
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Content
- 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
