逐步聚类法及其应用

本文介绍了一种非等级分类方法——逐步聚类法，并将其应用于翅果油树灌丛的数量分类研究，结果表明：逐步聚类法实现最优分类的目标过程，是依样方组内具有最小的离差平方和。样方组间具有最大的离差平方和为标准，使样方组内具有最大的同质性，样方组间具有最大的异质性，其分类结果与实际情况吻合度较高；其次，逐步聚类法只需计算每个样方到该样方形心的距离，可缩短计算时间和节省计算机内存单元，提高工作效率。 与模糊c—均值聚类和TWINSPAN结果相比，逐步聚类的结果类似于模糊c—均值聚类，即样方组内具有较高的同质性；在不要求分类结果具有明显上下级关系的前提下，逐步聚类结果要优于TWINSPAN。

STEPWISE CLUSTERING AND ITS APPLICATION TO VEGETATION CLASSIFICATION

Stepwise clustering, one of the methods for non-hierarchical classification, has been introduced in this paper, and was applied to the classification of Elaeagnusmollis community in Shanxi. The results show that the stepwise clustering accomplished the objective process of the optimal classification through minimizing the sum of the squared deviations within plot group and by maximizing the sum of the squared deviations between plot groups.This led to minimum homogeneity within plot group and maximum heterogeneity between plot groups. The results of stepwise clustering tallies with the reality. Furthermore,it allows the work more efficient because we only need calculate the centroid distance from one sample to another. Compared with fuzzy c-means algorithm and with TWINSPAN,the result of stepwise clustering is similar to that of fuzzy c-means algorithm which has greater homogeneity within plot group.In addition, the stepwise clustering is superior to the TWINSPAN procedure, providing that the classification results do not require an obvious hierarchy among plot groups.