Transactions on Rough Sets I James F. Peters - Andrzej Skowron, Editors-in-Chief /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Peters, James F. (Editor), Skowron, Andrzej (Editor), Grzymała-Busse, Jerzy W. (Editor), Kostek, Bożena (Editor), Świniarski, Roman W. (Editor), Szczuka, Marcin S. (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2004.
• Series: Lecture Notes in Computer Science, 3100
• Subjects: Computer science.; Computers.; Mathematical logic.; Database management.; Data mining.; Artificial intelligence.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Data Mining and Knowledge Discovery.; Computation by Abstract Devices.; Mathematical Logic and Formal Languages.; Database Management.; Pattern Recognition.

Table of Contents:

• 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.