Transactions on Rough Sets VIII (Lecture Notes in Computer Science, 5084)

VolumeVIIIoftheTransactions on Rough Sets (TRS)containsa widespectrum of contributions to the theory and applications of rough sets. The pioneering work by Prof. Zdzis law Pawlak led to the introduction of knowledge representation systems during the early 1970s and the discovery of rough sets during the early 1980s. During his lifetime, he nurtured worldwide interest in approximation, approximate reasoning, and rough set theory and its 1 applications. Evidence of the in?uence of Prof. Pawlak's work can be seen in the growth in the rough-set literature that now includes over 4000 publications 2 by more than 1900 authors in the rough set database as well as the growth and 3 maturity of the International Rough Set Society. This volume of TRS presents papers that introduce a number of new - vances in the foundations and applications of arti?cial intelligence, engineering, logic, mathematics, and science. These advances have signi?cant implications in a number of researchareas.In addition, it is evident from the papers included in this volume that roughset theoryand its application forma veryactiveresearch area worldwide. A total of 58 researchers from 11 countries are represented in this volume, namely, Australia, Canada, Chile, Germany, India, Poland, P.R. China, Oman, Spain, Sweden, and the USA. Evidence of the vigor,breadth, and depth of research in the theory and applications rough sets can be found in the articles in this volume. This volume contains 17 papers that explore a number of research streams. Title Page Preface Organization Table of Contents Some Rough Consequence Logics and their Interrelations Introduction Two Variants of {\it S}5 The “Modus Ponens” (MP) Rules The Systems {\it Lr_{i}} $Lr_1~ \sim~ Lr_5~ \sim~ Triv$ $Lr_2~\sim ~Lr_{\sim\kern-2pt>}~\sim ~Lr_{\approx}~\sim ~S5(2)~\prec ~Lr_1$ $Lr_4~\prec~Lr_3~\sim~Lr_0~\sim~S5(1)~\prec~Lr_2$} The Banerjee and Chakraborty Systems, Ja ́skowski’s $\it {J}$ and the Systems $\it {Lr_{i}}$ Some Extended Systems The + Systems Conclusions References Local and Global Approximations for Incomplete Data Introduction BasicNotions Incomplete Data Sets Blocks of Attribute-Value Pairs Definability Local Approximations Global Approximations Conclusions References A Rough Set Approach to Multiple Criteria ABC Analysis Introduction Multiple Criteria Decision Analysis A Rough Set Approach to MCABC A Dominance-Based Rough Set Theory for MCABC Decision Rules for MCABC Application Background Decision Rule Generation Comparison Comparison of Classification Results Conclusions References Partially Ordered Monads and Rough Sets Introduction Monads and Submonads Examples of Monads The Powerset Monad Powerset Monads with Fuzzy Level Sets The Covariant Double Contravariant Powerset Monad The Fuzzy Filter Monad Basic Triples and Partially Ordered Monads Examples of Partially Ordered Monads The Crisp Powerset Monad The Fuzzy Powerset Monad Powerset Monads with Fuzzy Level Sets The Covariant Double Contravariant Powerset Monad and the Partially Ordered Fuzzy Filter Monad Previous Work on Partially Ordered Monads for Fuzzy Convergence Extension Structures $\it {\Phi}$-Cauchy Structures and Completions Compactness as Completeness and Monadic Compactifications Applications to Rough Sets Relations, Fuzzy Relations and Kleisli Categories Ordinary Relations and Rough Sets Inverse Relations Monadic Relations and Rough Monads Conclusions References A Rough Sets Approach to the Identification and Analysis of Factors Affecting Biological Control of Leafy Spurge Introduction The Rough Set Approach Approximation Space Rough Approximations Information Tables Dependency Analysis and Data Reduction Computation of Rules Experiments Experimental Procedure Data Collection and Usage Results and Analysis Data Modelling and Analysis Release Factors Physical Factors Ecological Factors Vegetation Factors Combined Factors Conclusion References Interpretation of Extended Pawlak Flow Graphs Using Granular Computing Introduction Related Work Flow Graphs and Its Extension Flow Graph An Extension of Flow Graph Relationship Between EFG and GrC Granulation Decomposition and Composition of Granules Inference and Reformation Reduction of EFG Reformation Method Inference Method Simulation Experiments Conclusion References Generalized Indiscernibility Relations: Applications for Missing Values and Analysis of Structural Objects Introduction Knowledge Representation Reality Perceived by Sensors Semantics of Knowledge Language Information Systems Complete Data Incomplete Data Multivalued Attributes Structural Objects Sequential Data Processing Syntactic Rules Data Sequence Representation Parser Algorithm Semantic Values of Grammar Symbols Semantic Attachments Set Approximations Conclusions References A Categorical Approach to Mereology and Its Application to Modelling Software Components Introduction Standard Mereology Elements of Category Theory Introduction to Mereocat Mereocat Sums Independent Sum Interactive Sum Generalised Sum Mereocat Product Categorical Connector Framework Software Components and Mereocat Conclusion References Esoteric Rough Set Theory: Algebraic Semantics of a Generalized VPRS and VPFRS Introduction Generalized Covers Approach Concrete Katrinak Algebras Esoteric Rough Set Theory Exceptional Sets Equalities in Esoteric Rough Set Theory Connections with Equalities in the Rough Context and Dynamic Extensions Generalized Esoteric Covers Three Algebraic Semantics Examples Esoteric Rough Set Theory, VPRS and VPRFS Relativised Approximations Conclusion References Domain Knowledge Assimilation by Learning Complex Concepts Introduction Knowledge Elicitation from External Expert Ontology Matching Analysis of Outlier Cases Implementation Conclusion References Information Quanta and Approximation Operators: Once More Around the Track Introduction Information Structures and Approximation Operators Approximation Operators, Heyting-Brouwer Algebras and Rough Sets Concluding Remarks References On Partial Covers, Reducts and Decision Rules Introduction Partial Covers Main Notions Known Results On Polynomial Approximate Algorithms Bounds on C_{min}($\alpha$) Based on Information About Greedy Algorithm Work Upper Bound on C_{greedy}($\alpha$) On Covers for the Most Part of Set Cover Problems Partial Tests and Reducts Main Notions Relationships between Partial Covers and Partial Tests On Precision of Greedy Algorithm On Polynomial Approximate Algorithms Bounds on R_{min}($\alpha$) Based on Information About Greedy Algorithm Work Upper Bound on R_{greedy}($\alpha$) On Tests for the Most Part of Binary Decision Tables Partial Decision Rules Main Notions Relationships between Partial Covers and Partial Decision Rules On Precision of Greedy Algorithm On Polynomial Approximate Algorithms Bounds on L_{min}($\alpha$) Based on Information About Greedy Algorithm Work Upper Bound on L_{greedy}($\alpha$) On Decision Rules for the Most Part of Binary Decision Tables Conclusions References Evolutionary Rough k-Medoid Clustering Introduction Rough k-Means Algorithms Basic Properties of Rough Sets Lingras’ Rough k-Means Cluster Algorithm Mitra’s Evolutionary Extension of the Rough k-Means Classic k-Medoid Clustering The Algorithm Comparison of k-Medoids and k-Means Evolutionary Rough k-Medoids Rough k-Medoids Algorithms Objective Functions An Evolutionary Extension of the Rough k-Medoids Experiments Synthetic Data Colon Cancer Data Forest Data Control Chart Data Conclusion References The Rough Set Database System Introduction Capabilities of the System Home Page Adding Data – Online Searching for Data Editing the Existing Data The Classification of Publications with the Use of a Defined Classificator Registration of Users into the System Saving Data in a File Sending Files with Data to an Administrator Handling the Users Comments Statistics Help FAQ Software People Opinions Interactive Map of the World Plans for the Future Conclusions References A Model of User-Oriented Reduct Construction for Machine Learning Introduction User Preference of Attributes Quantitative Judgement of Attributes Qualitative Judgement of Attributes Connections between Quantitative and Qualitative Judgements of Attributes User Preference of Attribute Sets Basic Properties Quantitative Judgement of Attribute Sets Qualitative Judgement of Attribute Sets User Preference of Reducts Preliminaries The Deletion Algorithm The Addition Algorithm Conditional User Preferences Conditional User Preference of Attributes Reduct Construction Based on Conditional Preferences Conclusion References Research on Rough Set Theory and Applications in China Introduction Development of the Chinese Rough Set and SoftComputing Society Organization of CRSSC Key Research Groups of CRSSC General Status of Research on Rough Set Theory and Applications in China Research on Rough Set Theory and Its Applications in China Fundamentals of Rough Sets Knowledge Acquisition Granular Computing Based on Rough Sets Extended Rough Set Models Rough Logic Applications of Rough Sets Summary References Rough Neural Fault Classification of Power System Signals Introduction Power System Fundamentals Power Systems Power System Faults Mathematics Underlying Fault Classification and Recognition Techniques Rough Set Theory Classifier Fusion Theory Technology Review of Power System Fault Classification (PSFC) Wavelet Applications in Power Systems Combination of the Wavelet and Neural Network Techniques for Fault Detection Time-Frequency Representation Technique for Classifying Power Quality Disturbances Data Preparation for Manitoba Hydro HVDC PSFC Data Conversion Signal Grouping Signal Preprocessing and Feature Extraction for PSFC Signal Characteristics in Normal Condition Feature Extraction of 12 Types of Faults Rough Membership Neural Network (rmNN) for PSFC Sample Information System For PSFC Rough Membership Functions Rough Membership Tables for rmNN Training and Verification Design of rmNNs for PSFC Effects of the Number of Neurons in the Hidden Layer Effects of Learning Cycles, Learning Rate and Least Square Error Implementation of rmNN for PSFC Normal Artificial Neural Network (ANN) for PSFC A Single ANN for PSFC Twelve Sub-ANNs for PSFC Classifier Fusion Motivation in Using a Second Complementary Classifier Linear Mean-Deviation (LMD) Based Classifier Correlation of the rmNN and LMD Classifier Results of the rmNN and LMD Classifier Fusion Conclusion References Author Index Annotation 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. This book, which constitutes the eighth volume of the Transactions on Rough Sets series, contains a wide spectrum of contributions to the theory and applications of rough sets. The 17 papers presented explore several research streams and introduce a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science