On the application of cluster analysis to growing season precipitation data in North America east of the Rockies

Abstract

In this paper, after a review of a large number of applications on cluster analysis, an intercomparison of various cluster techniques was carried out on a well-studied dataset (7-day precipitation data from 1949 to 1987 in central and eastern North America). Twenty-two of the 23 cluster algorithms yielded grouping solutions. Monte Carlo simulations were undertaken to examine the reliability of the cluster solutions. This was done by bootstrap resampling from the full dataset with four different sample sizes, then testing significance by the t test and the minimum significant difference test. Results showed that nonhierarchical methods outperformed hierarchical methods. The rotated principal component methods were found to be the most accurate methods, the nucleated agglomerative method was found to be superior to all other hard cluster methods, and Ward's method performed best among the hierarchical methods. -from Authors