Using data mining techniques to improve inflation rate management

Document Type : RESEARCH PAPER

Authors

1 Master of Computer Science, Department of Computer Engineering Golestan University, Gorgan, Iran

2 Associate Professor, Department of Computer Engineering, Golestan University, Gorgan, Iran

Abstract

Many economists and sociologists believe that the inflation rate is more important than other economic indicators because inflation has many effects on various economic, social and political aspects of the society. With this approach, the present research identifies bottleneck commodities which correct pricing them are more important in the inflation rate control. In order to achieve this goal, 12 groups of basic commodities of the country, which are the main commodities that make up the Consumer Price Index (CPI), were collected and presented in a complete graph. For each node that represents a group of basic commodities, five features were considered. The features are the annual inflation related to each group, the degree of influence in increasing the total index, the number of subgroups of each main group, the degree of dependency, and the priority (level of demand). Then, by running the traveling salesman algorithm on the graph, we found a path where the group of foods and beverages is the bottleneck of the research subject. The results of the present study show that the management of pricing this group have an effective impact on the inflation control of other groups over time.

Keywords

Main Subjects

References

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