We would like to present, with great pleasure, the ?rst volume of a new jo- nal, Transactions on Rough Sets. This journal, part of the new journal subline in the Springer-Verlag series Lecture Notes in Computer Science, is devoted to the entire spectrum of rough set related issues, starting from logical and ma- ematical foundations of rough sets, through all aspects of rough set theory and its applications, 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, theory of evidence, etc. The ?rst, pioneering papers on rough sets, written by the originator of the idea,ProfessorZdzis lawPawlak,werepublishedintheearly1980s.Weareproud to dedicate this volume to our mentor, Professor Zdzis law Pawlak, who kindly enriched this volume with his contribution on philosophical, logical, and mat- matical foundations of roughset theory. In his paper Professor Pawlakshows all over again the underlying ideas of rough set theory as well as its relations with Bayes¿ theorem, con?ict analysis, ?ow graphs, decision networks, and decision rules.

Rough Sets - Introduction.- Some Issues on Rough Sets.- Rough Sets - Theory.- Learning Rules from Very Large Databases Using Rough Multisets.- Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction.- Generalizations of Rough Sets and Rule Extraction.- Towards Scalable Algorithms for Discovering Rough Set Reducts.- Variable Precision Fuzzy Rough Sets.- Greedy Algorithm of Decision Tree Construction for Real Data Tables.- Consistency Measures for Conflict Profiles.- Layered Learning for Concept Synthesis.- Basic Algorithms and Tools for Rough Non-deterministic Information Analysis.- A Partition Model of Granular Computing.- Rough Sets - Applications.- Musical Phrase Representation and Recognition by Means of Neural Networks and Rough Sets.- Processing of Musical Metadata Employing Pawlak's Flow Graphs.- Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values.- Rough Sets and Relational Learning.- Approximation Space for Software Models.- Application of Rough Sets to Environmental Engineering Models.- Rough Set Theory and Decision Rules in Data Analysis of Breast Cancer Patients.- Independent Component Analysis, Principal Component Analysis and Rough Sets in Face Recognition.