الفهرس 
Basic ConceptsOnly 14 pages are availabe for public view
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Abstract
Algebraic structures, arising from logic, have been of greet influence in different areas of computer science.
More importantly, results from algebra can be used to enrich studies in different branches of computer science such as Artificial Intelligence, Machine Learning, Data design, Data Mining, Logic Programming and many others.
Rough set theory is a new mathematical approach to intelligent data analysis and data mining. It has received considerable attention in recent years for mining hidden deterministic rules from a data base. 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. It was originated by Pawlak in 1980. After 27 years of pursuing rough set theory and its applications the approach reached a certain degree of maturity. In recent years we witness a rapid growth of interest in rough set theory and its applications, world wide. Many international workshops, conferences and seminars included rough sets in their programs. Thousands of papers and several books have been published until now on various aspects of rough sets. Basics of rough sets can be found in [34], [36] and [38].
In the first part of this thesis ( Chapters 1 and 2 ) we remark upon some relationships between the ideas of information systems and rough sets.
An algebraic approach to rough sets was first proposed by Iwinski [20]. Iwinski’s aim, later extended by Pomykala [39] and modified by Comer [7], was to endow the rough subsets of a universe U with a natural algebraic structure. It turns.