Title: A data mining framework for classification of organisational performance based on rough set theory

Authors: Hamid Hasani; Seyed Mohammad Jafar Jalali; Danial Rezaei; Mohsen Maleki

Addresses: Faculty of Management, College of Farabi, University of Tehran, Tehran, Iran ' Computer and Information Science Department, University of Massachusetts Dartmouth, Massachusetts, USA ' Faculty of Management, College of Farabi, University of Tehran, Tehran, Iran ' Department of Statistics, Shiraz University, Shiraz, Iran

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.

Keywords: rough set theory; RST; data mining; organisational performance.

DOI: 10.1504/AJMSA.2018.091020

Asian Journal of Management Science and Applications, 2018 Vol.3 No.2, pp.156 - 180

Received: 16 Jun 2017
Accepted: 18 Nov 2017

Published online: 06 Apr 2018 *

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