Transactions on Rough Sets XI [electronic resource] / edited by James F. Peters, Andrzej Skowron.

Mining Numerical Data – A Rough Set Approach -- Definability and Other Properties of Approximations for Generalized Indiscernibility Relations -- Variable Consistency Bagging Ensembles -- Classical and Dominance-Based Rough Sets in the Search for Genes under Balancing Selection -- Satisfiability Judgement under Incomplete Information -- Irreducible Descriptive Sets of Attributes for Information Systems -- Computational Theory Perception (CTP), Rough-Fuzzy Uncertainty Analysis and Mining in Bioinformatics and Web Intelligence: A Unified Framework -- Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms -- A Distributed Decision Rules Calculation Using Apriori Algorithm -- Decision Table Reduction in KDD: Fuzzy Rough Based Approach.

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. The 10 articles contained in this volume introduce a number of advances in the foundations and applications of rough sets. The topics covered include calculus of attribute-value pairs useful in mining numerical data; definability and coalescence of approximations; a variable consistency generalization approach to bagging, controlled by measures of consistency;the use of classical and dominance-based rough sets in the search for genes; judgement about satisfiability with incomplete information; irreducible descriptive sets of attributes for information systems useful in the design of concurrent data models; computational theory of perceptions (CTP) and its characteristics and relation with fuzzy-granulation; methods and algorithms of Net-processing; and decision table reduction methods based on fuzzy rough sets.