Unified knowledge economy competitiveness index using fuzzy clustering model

The aim of this paper is to design a unified knowledge economy competitiveness index using fuzzy clustering, to aggregate four of the most reputable and famous knowledge economy indicators into a unified index that reflects the overall rate of knowledge in an economy, to serve many purposes for the decision makers and foreign investors interested in such economy. The four selected indices are: Knowledge Economy Index (KEI) from World Bank, Information and Communication Technologies Development Index (IDI) from United Nations agency for information and communication technology issues (ITU), Global Competitiveness Index (GCI) from the World Economic Forum, and World Competitiveness Yearbook (WCY) from Institute for Management Development (IMD). To achieve this unified index, a four steps framework is proposed. The first step utilizes a Correlation analysis, the second step is to carry a Principle Component Analysis (PCA) analysis and the third step employs training an Adaptive Neural Fuzzy Inference Systems (ANFIS) and the forth step is to create a unified index based on all existing indices. The purpose of the first step was to test the relationship between the selected indices and how strong it is. The PCA is employed to test the similarity amongst existing indices and whether they can be reduced in any form. ANFIS was used to generate rules to create trained submodel that determine which of the input indices make efficient contribution to the new unified knowledge indicator. Then, the fuzzy c-means clustering technique is used to construct the new Unified Knowledge Competitiveness and Progress Indicator (UKPI) which combines the four selected aggregate indices into a new single meaningful index that reflects the overall rate of Knowledge competitiveness and progress in a nation.