Fuzzy association rule mining for economic development indicators

Abstract

This paper is focused on fuzzy mining approach to extract fuzzy association rules among the economic development indicators that are net official development assistance received (ODA), foreign direct investment (FDI) and gross domestic product (GDP) for developing country India. This study is an attempt to explore the use of fuzzy association rule mining on time series data and to extract interesting association rules therefrom. The extracted rules exhibit the relative volatile nature of these three development indicators. A fuzzy membership function is used to transform the quantitative values of percentage change of each successive year datum into fuzzy sets in linguistic terms. The scalar cardinality of each linguistic term is calculated on the yearly data. Only those fuzzy association rules that qualified the criteria of minimum support and minimum confidence value were taken into consideration. The rules thus mined out exhibit quantitative regularities and can be used for the better suggestion to appropriate policy makers.

Linguistic frequent pattern mining using a compressed structure

“…The method is then evaluated regarding consistency and efficacy with different aspects, which handles veracity issues and is efficient in reducing instances. Several algorithms based on the fuzzy-set theory for mining the required information in different applications and domains were then studied and developed in progress [5,11,19,27,38,43].…”