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中文摘要：
在典型的空间聚类算法尽平均法和尽中心法中，K一般为用户事先确定的值，然而，实际中K值很难被精确地确定，往往表现为一个模糊的取值区间。在此提出距离代价函敖的概念，建立了相应的数学模型并设计了一个新的K值优化算法，对空间聚类K值优化问题进行了初步的研究。
英文摘要：
The value of K is always confirmed in advance to exert K-means algorithm of spatial clustering. However, it can not be clearly and easily confirmed in fact for its uncertainty. A distance cost function was recommended. A corresponding math model was set up and a new optimization algorithm of K value was designed. A preliminary study on the optimization of K value for spatial clustering was realized by a simulation design.
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