Zdzislaw Pawlak

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Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery(More)
Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In(More)
Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes(More)
Some basic concepts concerning information systems are defined and investigated. With every information system a query language is associated and its syntax and semantics is formally defined. Some elementary properties of the query language are stated. The presented approach leads to a new information systems organization. The presented idea was implemented(More)
The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory – nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many real-life applications of the theory have proved its practical usefulness. The paper(More)
The issue of knowledge representation and the method of inferring decision rules are of fundamental nature in the design of intelligent systems. When knowledge of the system is sufficient and precise (without uncertainty), many problems in artificial intelligence can be successfully modelled by techniques such as first order logic (Kowalski, 1979; Barr &(More)