Application of Rough Set Theory in Data Mining for Decision Support Systems (DSSs)

سال انتشار: 1386
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 338

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شناسه ملی سند علمی:

JR_JOIE-1-1_004

تاریخ نمایه سازی: 22 آبان 1397

چکیده مقاله:

Decision support systems (DSSs) are prevalent information systems for decision making in many competitive business environments. In aDSS, decision making process is intimately related to some factors which determine the quality of information systems and their relatedproducts. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers need someDSS tools for rapid decision making. In traditional approaches to decision making, usually scientific expertise together with statisticaltechniques are needed to support the managers. However, these approaches are not able to handle the huge amount of real data, and theprocesses are usually very slow. Recently, several innovative facilities have been presented for decision making process in enterprises.Presenting new techniques for development of huge databases, together with some heuristic models have enhanced the capabilities of DSSsto support managers in all levels of organizations. Today, data mining and knowledge discovery is considered as the main module ofdevelopment of advanced DSSs. In this research, we use rough set theory for data mining for decision making process in a DSS. Theproposed approach concentrates on individual objects rather than population of the objects. Finally, a rule extracted from a data set and thecorresponding features (attributes) is considered in modeling data mining.

نویسندگان

Mohammad Hossein Fazel Zarandi

Department of Industrial Engineering, Amir kabir university of Technology, Tehran, Iran

Abolfazl Kazemi

Department of Industrial Engineering, Amir kabir university of Technology, Tehran, Iran