New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
566
Utgivningsdatum
1999-10-01
Upplaga
1999 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Zhong, Ning (ed.), Skowron, Andrzej (ed.), Ohsuga, Setsuo (ed.)
Illustratör/Fotograf
Illustrations
Illustrationer
XIV, 566 p.
Dimensioner
234 x 156 x 30 mm
Vikt
804 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540666455

New Directions in Rough Sets, Data Mining, and Granular-Soft Computing

7th International Workshop, RSFDGrC'99, Yamaguchi, Japan, November 9-11, 1999 Proceedings

Häftad,  Engelska, 1999-10-01
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This volume contains the papers selected for presentation at the Seventh Int- national Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (RSFDGrC'99) held in the Yamaguchi Resort Center, Ube, Y- aguchi, Japan, November 9-11, 1999. The workshop was organized by Inter- tional Rough Set Society, BISC Special Interest Group on Granular Comp- ing (GrC), Polish-JapaneseInstitute of Information Technology,and Yamaguchi University. RSFDGrC'99providedaninternationalforumforsharingoriginalresearch results and practical development experiences among experts in these emerging ?elds.An importantfeatureoftheworkshopwasto stresstheroleofthe integ- tion of intelligent information techniques. That is, to promote a deep fusion of these approaches to AI, Soft Computing, and Database communities in order to solve real world, large, complex problems concerned with uncertainty and fuz- ness. In particular, rough and fuzzy set methods in data mining and granular computing were on display. The total of 89 papers coming from 21 countries and touching a wide spectrum of topics related to both theory and applications were submitted to RSFDGrC'99. Out of them 45 papers were selected for regular presentations and 15 for short presentations. Seven technical sessions were organized, namely: Rough Set Theory and Its Applications; Fuzzy Set Theory and Its Applications; Non-Classical Logic and Approximate Reasoning; Information Granulation and Granular Computing; Data Mining and Knowledge Discovery; Machine Lea- ing; Intelligent Agents and Systems. TheRSFDGrC'99programwasenrichedbyfourinvitedspeakers:Zdzis law Pawlak, Lot? A. Zadeh, Philip Yu, and Setsuo Arikawa, from Soft Computing, Database, and AI communities. A special session on Rough Computing: Fo- dations and Applications was organized by James F. Peters.
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Innehållsförteckning

Invited Talks.- Decision Rules, Bayes Rule and Rough Sets.- A New Direction in System Analysis: From Computation with Measurements to Computation with Perceptions.- On Text Mining Techniques for Personalization.- A Road to Discovery Science.- Rough Computing: Foundations and Applications.- Calculi of Granules Based on Rough Set Theory: Approximate Distributed Synthesis and Granular Semantics for Computing with Words.- Discovery of Rules about Complications.- Rough Genetic Algorithms.- Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach.- Rough Set Theory and Its Applications.- Toward Spatial Reasoning in the Framework of Rough Mereology.- An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information.- On the Extension of Rough Sets under Incomplete Information.- On Rough Relations: An Alternative Formulation.- Formal Rough Concept Analysis.- Noise Reduction in Telecommunication Channels Using Rough Sets and Neural Networks.- Rough Set Analysis of Electrostimulation Test Database for the Prediction of Post-Operative Profits in Cochlear Implanted Patients.- A Rough Set-Based Approach to Text Classification.- Modular Rough Fuzzy MLP: Evolutionary Design.- Approximate Reducts and Association Rules.- Handling Missing Values in Rough Set Analysis of Multi-attribute and Multi-criteria Decision Problems.- The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings.- Rough Problem Settings for Inductive Logic Programming.- Using Rough Sets with Heuristics for Feature Selection.- The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm.- Fuzzy Set Theory and Its Applications.- Level Cut Conditioning Approach to the Necessity MeasureSpecification.- Four c-Regression Methods and Classification Functions.- Context-Free Fuzzy Sets in Data Mining Context.- Applying Fuzzy Hypothesis Testing to Medical Data.- Generating a Macroeconomic Fuzzy Forecasting System Using Evolutionary Search.- Fuzzy Control of Nonlinear Systems Using Nonlinearized Parameterization.- Control of Chaotic Systems Using Fuzzy Model-Based Regulators.- Fuzzy Behavior-Based Control for the Obstacle Avoidance of Multi-link Manipulators.- Fuzzy Future Value and Annual Cash Flow Analyses.- Semi-linear Equation with Fuzzy Parameters.- Non-classical Logic and Approximate Reasoning.- First Order Rough Logic-Revisited.- A Generalized Decision Logic in Interval-Set-Valued Information Tables.- Many-Valued Dynamic Logic for Qualitative Decision Theory.- Incorporating Fuzzy Set Theory and Matrix Logic in Multi-layer Logic.- Fuzzy Logic as Interfacing Media for Constraint Propagation Based on Theories of Chu Space and Information Flow.- Pattern Reasoning: A New Solution for Knowledge Acquisition Problem.- Probabilistic Inference and Bayesian Theorem Based on Logical Implication.- Reasoning with Neural Logic Networks.- The Resolution for Rough Propositional Logic with Lower (L) and Upper (H) Approximate Operators.- Information Granulation and Granular Computing.- Information Granules in Distributed Environment.- Evolving Granules for Classification for Discovering Difference in the Usage of Words.- Interval Evaluation by AHP with Rough Set Concept.- Interval Density Functions in Conflict Analysis.- Incorporating Personal Database Unification by Granular Computing.- Data Mining and Knowledge Discovery.- Knowledge-Driven Discovery of Operational Definitions.- A Closest Fit Approach to Missing Attribute Values in Preterm Birth Data.- Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density.- A Distance-Based Clustering and Selection of Association Rules on Numeric Attributes.- Knowledge Discovery for Protein Tertiary Substructures.- Integrating Classification and Association Rule Mining: A Concept Lattice Framework.- Using Rough Genetic and Kohonens Neural Network for Conceptual Cluster Discovery in Data Mining.- Towards