الفهرس 
Introduction
Decission rulesOnly 14 pages are availabe for public view
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Abstract
A decision support system is a way to model data and make quality decisions based upon it. Making the right decision in business is usually based on the quality of your data and your ability to sift through and analyze the data to find trends in which you can create solutions and strategies. DSSs or decision support systems are usually computer applications along with a human component that can sift through large amounts of data and pick between the many choices. Rough set theory proposed by professor Z.Pawlak in 1982 has been applied to many fields. It is a valid mathematical method to deal with imprecise, uncertain, and vague information. The basic ideas of rough sets have already been explored in many fields, such as soft computing, knowledge discovery, machine learning, and web intelligence. Professor Z.Pawlak has done a lot of work in rough sets and its applications including rough sets in data mining and decision support systems. Some basic ideas of rough sets have been successfully explored in decision support systems and machine learning in his work. In the second chapter, we give a comprehensive survey for the basic concepts of rough sets and decision support systems, and then we use rough sets for combing decisions by examples as shown in chapter three. This allowed us to evaluate the quality of decision and level of consistency in data. Taking advantage of some useful proprieties of rough set, we proposed combiner algorithm to combine lower and upper approximations. Our experimental results indicate that combining lower and upper approximations improve the quality of decision rules extracting from data. It also, improves the classification accuracy measured to group decision makers. In chapter four, we make a comparison between the our method for our learning method on decision support systems based on rough sets and traditional learning classifiers in many real data sets. This allowed us to improve the quality of decision rule and level of consistency in data. Taking advantage of some useful proprieties of rough set, we use LERS system to improve knowledge discovery on DSS. Our experimental results indicate that constructing DSS based on rough set improve the quality of decision rules which extracting from data. It also, improves the classification accuracy computed by classifiers. Finally, the thesis is concluded in chapter 5, with a summary of the key findings from the research conducted here.