Rough Set Theory and Granular Computing

Inbunden, Engelska, 2003

Av Masahiro Inuiguchi, Shusaku Tsumoto, Shoji Hirano

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This monograph presents novel approaches and new results in fundamentals and applications related to rough sets and granular computing. It includes the application of rough sets to real world problems, such as data mining, decision support and sensor fusion. The relationship of rough sets to other important methods of data analysis - Bayes theorem, neurocomputing and pattern recognition is thoroughly examined. Another issue is the rough set based data analysis, including the study of decision making in conflict situations. Recent engineering applications of rough set theory are given, including a processor architecture organization for fast implementation of basic rough set operations and results concerning advanced image processing for unmanned aerial vehicles. New emerging areas of study and applications are presented as well as a wide spectrum of on-going research, which makes the book valuable to all interested in the field of rough set theory and granular computing.

Produktinformation

Utgivningsdatum2003-04-22
Mått155 x 235 x 22 mm
Vikt647 g
FormatInbunden
SpråkEngelska
SerieStudies in Fuzziness and Soft Computing
Antal sidor300
Upplaga2003
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540005742

Tillhör följande kategorier

Tillämpad matematik inom Naturvetenskap och teknik
Tillämpad datateknik inom Data och it
Systemvetenskap och AI inom Data och it
Teknik: allmänt inom Naturvetenskap och teknik

Bayes’ Theorem — the Rough Set Perspective.- 1 Introduction.- 2 Bayes’ Theorem.- 3 Information Systems and Approximation of Sets.- 4 Decision Language.- 5 Decision Algorithms.- 6 Decision Rules in Information Systems.- 7 Properties of Decision Rules.- 8 Decision Tables and Flow Graphs.- 9 Illustrative Example.- 10 Conclusion.- References.- Approximation Spaces in Rough Neurocomputing.- 1 Introduction.- 2 Approximation Spaces in Rough Set Theory.- 3 Generalizations of Approximation Spaces.- 4 Information Granule Systems and Approximation Spaces.- 5 Classifiers as Information Granules.- 6 Approximation Spaces for Information Granules.- 7 Approximation Spaces in Rough-Neuro Computing.- 8 Conclusion.- References.- Soft Computing Pattern Recognition: Principles, Integrations and Data Mining.- 1 Introduction.- 2 Relevance of Fuzzy Set Theory in Pattern Recognition.- 3 Relevance of Neural Network Approaches.- 4 Genetic Algorithms for Pattern Recognition.- 5 Integration and Hybrid Systems.- 6 Evolutionary Rough Fuzzy MLP.- 7 Data mining and knowledge discovery.- References.- I. Generalizations and New Theories.- Generalization of Rough Sets Using Weak Fuzzy Similarity Relations.- Two Directions toward Generalization of Rough Sets.- Two Generalizations of Multisets.- Interval Probability and Its Properties.- On Fractal Dimension in Information Systems.- A Remark on Granular Reasoning and Filtration.- Towards Discovery of Relevant Patterns from Parameterized Schemes of Information Granule Construction.- Approximate Markov Boundaries and Bayesian Networks: Rough Set Approach.- II. Data Mining and Rough Sets.- Mining High Order Decision Rules.- Association Rules from a Point of View of Conditional Logic.- Association Rules with Additional Semantics Modeled by BinaryRelations.- A Knowledge-Oriented Clustering Method Based on Indiscernibility Degree of Objects.- Some Effective Procedures for Data Dependencies in Information Systems.- Improving Rules Induced from Data Describing Self-Injurious Behaviors by Changing Truncation Cutoff and Strength.- The Variable Precision Rough Set Inductive Logic Programming Model and Future Test Cases in Web Usage Mining.- Rough Set and Genetic Programming.- III. Conflict Analysis and Data Analysis.- Rough Set Approach to Conflict Analysis.- Criteria for Consensus Susceptibility in Conflicts Resolving.- L1-Space Based Models for Clustering and Regression.- Upper and Lower Possibility Distributions with Rough Set Concepts.- Efficiency Values Based on Decision Maker’s Interval Pairwise Comparisons.- IV. Applications in Engineering.- Rough Measures, Rough Integrals and Sensor Fusion.- A Design of Architecture for Rough Set Processor.- Identifying Adaptable Components — A Rough Sets Style Approach.- Analysis of Image Sequences for the UAV.