A comparison of maximum covariance and K-means cluster analysis in classifying cases into known taxon groups.
A comparison of maximum covariance and K-means cluster analysis in classifying cases into known taxon groups.
复制标题
DOI：
10.1037/1082-989x.7.2.245
复制
发表时间：
2002-06
影响因子：
7
通讯作者：
Theodore P. Beauchaine;Robert J Beauchaine
中科院分区：
文献类型：
--
作者：
Theodore P. Beauchaine;Robert J Beauchaine
最大协方差（MAXCOV）是一种确定一组 3 个或更多指标是否标记个体的 1 个连续或 2 个离散潜在分布的方法。尽管已经明确了 MAXCOV 有效检测潜在类群的情况，但其将病例分组的效率尚未评估，并且很少有研究将其性能与聚类分析的性能进行比较。在目前的蒙特卡罗研究中，MAXCOV 和 k 均值算法的分类效率在样本大小、效应大小、指标数量、分类基础率和组内协方差的范围内进行了比较。当这些参数的影响最小化时，k 均值比 MAXCOV 正确分类了更多的数据点。然而，当所有参数的影响同时增加时，MAXCOV 的表现优于 k 均值。
Maximum covariance (MAXCOV) is a method for determining whether a group of 3 or more indicators marks 1 continuous or 2 discrete latent distributions of individuals. Although the circumstances under which MAXCOV is effective in detecting latent taxa have been specified, its efficiency in classifying cases into groups has not been assessed, and few studies have compared its performance with that of cluster analysis. In the present Monte Carlo study, the classification efficiencies of MAXCOV and the k-means algorithm were compared across ranges of sample size, effect size, indicator number, taxon base rate, and within-groups covariance. When the impact of these parameters was minimized, k-means classified more data points correctly than MAXCOV. However, when the effects of all parameters were increased concurrently, MAXCOV outperformed k-means.