Comparative Study Of Economic Regionalization Based On Clustering Analysis

Macro-economic regionalization is not only one of the traditional problems of regional economic research, but also a practical planning problem. It is an important basis of distributing productivity rationally and developing regional economic cooperation. Macro-economic regionalization occupies an important position in the economic development of a country. A scientific and rational economic regionalization can help analyzing the status of the economy, formulating economic planning, and promoting economic development. Because of the long-time tradition of planned economy and huge regional disparity, China emphasizes on economic regionalization particularly. Since 1950's, our country has promulgated a series of economic regionalization projects which have played positive impact on economic development under the planned economy. Along with the swift development of Chinese economy, more and more scholars come to realize the importance of numerical classification in partition of economic regions. Based on macro-economic statistical data of our country, this article does a deep research and daring attempt on the plan of economic regionalization and the mode of economic development of each province using traditional hierarchical clustering method and one qualitative biclustering method whose function is outstanding at present.. Research results show that hierarchical clustering method can be conveniently used in regionalization research of the nation and the integral analytical results are comparatively intuitionistic. Biclustering method has the particular advantage of measuring the similarity between provinces under specific conditions.First, this article summarizes the economic regionalization methods and results of previous study. It points out the limitation of existing methods. Then it proposes the research thinking and method of this article. The following part summarizes classification and basic principles of clustering algorithm and biclustering algorithm, especially emphasizes on the steps and features of a very popular biclustering algorithm QUBIC. In addition, it makes some improvement to this algorithm concerning to the characters of economical regionalization. After that, this article uses hierarchical clustering algorithm and improved QUBIC biclustering algorithm to analyze the macro- economic data from 1999 to 2007 of the 31 provinces in China. Then it discusses the results in multiple aspects. Finally, according to the discussion of the results, the thesis makes a conclusion of the economic regionalization plan in China.Especially, some innovations have been made in this article:1. According to the features of macro-economic data, this article applies two dimension-reduction methods, whose results retain the temporal character of the data. One type is generating new index with adding the year mark to each annual index. In this way, the dimension is reduced to two dimension space of new index and provinces. The other type is generating new provinces with adding year mark to each index of each province. In this way, the dimension is reduced to two dimension space of index and new provinces. Then two clustering algorithms are applied separately to each group of data and comparative study has been made to the results systematically.2. Most of clustering methods measure similarities between objects according to the similarity measuring function in order to classify the objects. These methods are based on all conditions. But the objects being clustered may not have a good relationship under all the conditions if there are too many. This problem may cause the reduction of specificity of the classification results. In order to overcome the limitations on this problem of general clustering methods, this article applies biclustering algorithm to the economic regionalization for the first time. Results show that one province may appear in different biclusters which solves the problem of each object belonging to only one cluster in traditional clustering methods. Another progress is that provinces in different clusters in former clustering results may be assigned to one cluster in biclustering results. That is to say, although there is a disparity between the integral developing levels of different provinces, it may appear similarity in some properties.3. This article also improves QUBIC biclustering algorithm. Based on the characters of economic data and principles of the algorithm, the writer improves the decretion method, which makes the comparation between objects more reliable and greatly improved the practical values of the analyzing results. 4. Heat map is applied to analysis of clustering results for the first time, which makes the analysis of results visualized. Tree map in heat map provides proximity between clustering objects using heat map composed of three colors of red, black and green representing three levels of good, middle and bad.