Pattern recognition based on weighted fuzzy C-means clustering

In data mining, fuzzy C-means clustering algorithm has demonstrated advantage in dealing with the challenges posed by large collections of vague and uncertain data. This paper reviews concept of fuzzy C-means clustering which is widely used in context of pattern recognition. Based on the study of the fuzzy C-means algorithm, we propose a weighted local fuzzy regression model. The efficiency of the new modified model is demonstrated on real data from 1980 to 2010 collected for road freight of China. And with this method, analysis of correlation between economic development, transport facilities and demand for road transport is given. At last, we apply this method to predict the demand for road freight of China from 2011 to 2013. The results shown that weighed fuzzy model has more practical value than least squares regression on prediction problems with the small sample, non-linear and high-dimensional pattern recognition of transport system.