An Approach of Association Rule Mining for Control Population Size by using Rough Set Theory

Abstract

Information technology is a turning point in strategic decision-making. In order to achieve resource efficiency, create equilibrium in the workforce of the community and develop macroeconomic policies, population control is must be. Achieving these great goals requires analyzing the information of past years and its uncertainty as well as the lost values. The analysis of past work and ongoing work shows that data mining experts do not address the issue of population control. In this paper, we present a comparative framework for the study of non-recognizable data items, using the theory of rough collections. Our next work in this paper is to evaluate the previous algorithms provided for the preprocessing, feature extraction, and exploration of community rules algorithms (Case Study: population statistics, presented at the Center for Statistics of Iran (ISC)). The results of the experiments show that the proposed method is both useful for strategic decision making in macroeconomic policies, and for the analysis of other demographic data.