Application Of Fuzzy Clustering In The Regional Economic Divide

In recent years, with the globalization of information technology and the internet, generating a large number of data leads to the phenomenon of information overload, data mining is just this tool to solve the problem, and clustering is one of representative data mining techniques. Cluster analysis was applied to data mining significant effect, with further research, however we have found that the classic either-or classification does not apply to fuzzy classification. Therefore, fuzzy clustering technique which combines of machine learning and fuzzy math has become the new darling of the clustering techniques, yet achieved outstanding results in the clustering precision.How to get a more accurate classification of those from the Statistical Yearbook of the vast economic statistics has become a problem,especially in the premise of no prior information.In this paper, based on fuzzy clustering algorithm (FCM),we draw a very efficient clustering results in six macroeconomic indicators as the basis for division.It has a strong practical reference value.The paper focus on the fuzzy clustering technique. The major achievements include the following three aspects:1. Introduce the concept of cluster analysis techniques and its requirements, further discussed some cluster analysis common methods. Describes the classical clustering algorithm-Kmeans clustering algorithm with a local optimum and convergence speed characteristics, but can not guarantee the global optimum and it is sensitive to initial, itsclustering precision is dissatisfied.2. Detailed study of fuzzy clustering technique, on the basis of fuzzy mathematics, we complete and test two fuzzy clustering algorithm based on fuzzy relations and fuzzy division. Fuzzy relations clustering can obtain the best classification corresponding optimal threshold or affiliated range, but prone to isolated point problem; The final output of fuzzy C-division clustering(FCM) is a membership matrix, both efficiency and accuracy, its clustering results with high interpretability.3. Making use of advantages of FCM, we complete an application in the regional economic divide of China 2013. The results reveal that in the past 2013, the development gap of three regions-west, middle and east still exists, this unbalanced regional development problem must draw our attention. Looking at the results from the classification, largely reflect the real situation of China’s regional economic development to proof the effectiveness of FCM. Only by using the data itself in the absence of any prior information, FCM gives us four kinds of regions with different levels of development. This is a meaningful result, can provide an initial judgment for the launch of the actual economic work and enhance the efficiency of this work.