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Economic indicators in economic policies have an important place in determining the levels of development. Determining and classifying the existing social and economic structures of countries is very important for examining the development states and possible development tendencies of countries and forming regional development policies in line with these. The aim in cluster analysis, is to classify datas in to similarity and perform useful knowledge for the researcher. Cluster analysis, which became more popular among the subjects of statistical classification in recent years, can give more reliable results when there is apriori knowledge about number of clusters. Fuzzy models interested in fuzzy model structures and try to estimate system behaviours that has no knowledge about their structure. Fuzzy Cluster Analysis is try to decompose the groups which membership degrees cannot be determined. When the number of datas and variables increased or cluster structures came to closer for all, Cluster analysis has given more successful results then the other cluster analysis methods. In this study, Turkish Republics were classified in terms of the indicators determined by using Fuzzy C-Means (FCM) and Gath Geva methods which are frequently used in fuzzy clustering analysis. The objective was to find out the common class structures of Turkish Republics which came out with the disintegration of the Soviet Union in 1991 and which experienced economic similar problems and thus to help countries in the same clusters in similar economic planning. Results are also compared between fuzzy and crisp clustering analysis methods.

Classification of Turkish Republics with Specific Economic Indicators in Fuzzy Clustering Analysis