Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering w...Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.展开更多
oceanographic data files on the China Seas prepared by the National Marine Data and Information Service, SOA, China and the '30-year (1953-1982) Reports of Sea Surface Monthly Mean Temperature in the East China Se...oceanographic data files on the China Seas prepared by the National Marine Data and Information Service, SOA, China and the '30-year (1953-1982) Reports of Sea Surface Monthly Mean Temperature in the East China Sea by the Meteorological Agency, Japan,' were used to calculate the digital characteristics of frequency distribution of sea and air temperature in 153 areas in the China Seas. Principal factor analysis and fuzzy cluster ISODATA were used to divide the China hydroclimatic area into three climatic zones including ten climatic regions. It is concluded that the characteristic values derived by this method may completely show the characteristics of frequency distribution of sea and air temperature in the studied area and the final division of hydroclimatic area is fully coincident with the author's former result [2].展开更多
基金Innovation Program of Shanghai Municipal Education Commission,China(No.12YZ191)
文摘Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.
文摘oceanographic data files on the China Seas prepared by the National Marine Data and Information Service, SOA, China and the '30-year (1953-1982) Reports of Sea Surface Monthly Mean Temperature in the East China Sea by the Meteorological Agency, Japan,' were used to calculate the digital characteristics of frequency distribution of sea and air temperature in 153 areas in the China Seas. Principal factor analysis and fuzzy cluster ISODATA were used to divide the China hydroclimatic area into three climatic zones including ten climatic regions. It is concluded that the characteristic values derived by this method may completely show the characteristics of frequency distribution of sea and air temperature in the studied area and the final division of hydroclimatic area is fully coincident with the author's former result [2].
