Application of K-means Cluster Based on Uncertain Illation of Cloud Model in Urban Land Gradation
Shasha Lu, Xingliang Guan, Qi Long Lu · International Workshop on Education Technology and Computer Science · 2011
As industrialization and urbanization accelerates, it is important to explore a superior urban land evaluation system. This paper puts forward a novel thinking and step of urban land gradation. Firstly, qualitative linguistic description of the uncertain urban land evaluation factor conditions are transformed into a fine-changeable cloud drops and mapped into quantitative values with the uncertain illation based on cloud model. Obviously, the cloud model is not a simple combination of probability and fuzzy method. Secondly, the weight values of land units are clustered, each of which features unite land price and its evaluation factors concrete status by the method of cloud model based on hierarchical clustering. Finally, it determines the corresponding land units levels. How to determine the accurate system of evaluation factors and their weights will be the key to successful application of this method. Furthermore, based on K-means cluster analysis and uncertain illation of cloud model, interesting insights are obtained on a case study of urban land gradation in Yongchun of Fujian Province to verify the feasibility of this method.