A data mining framework for classification of organisational performance based on rough set theory. (2018)

• Record Type: Journal Article
• Authors: Hasani, Hamid; Jalali, Seyed Mohammad Jafar; Rezaei, Danial; Maleki, Mohsen
• Is Part Of: Asian journal of management science and applications. Volume 3:Number 2(2017)
• Page Start: 156
• Page End: 180
• Publication Date: 2018
• Subjects: rough set theory -- RST -- data mining -- organisational performance; Management science -- Asia -- Periodicals; 658.009505
• Languages: English
• ISSNs: 2049-8683

Abstract

Today's organisations perform their activities in difficult situations with uncertainty, rapid changes of technology, global markets and etc. There are a lot of factors which affect their performances. In this study we mostly concentrate on qualitative factors to consider organisational performance. So, we use a data mining framework based on rough set theory (RST) for classification and description usage of the organisational performance in some Iranian petrochemical companies. The proposed framework consists of three stages: 1) problem definition and data collection; 2) RST analysis (rules generation and evaluation); 3) usage of derived rules. For this purpose, 28 Iranian petrochemical companies are considered. Ten most important factors which affect organisational performance are examined. Total number of indices is 28, so it makes this work, an exhaustive research study. There are two different usages of this study. One of them is classification (predictive) usage and the other is descriptive usage.