求人工翻译高手，英译汉，翻的好可以适当加分

Question

In contrast, a CA groups similar data into clusters by attempting to minimize the variability within each cluster and maximizing variability between clusters. The kmeans method is nonhierarchical supervised partitioning CA (Rencher 2002, 482). The number of clusters (k) is predetermined or supervised and a vector specifies the mean of a cluster, or centroid, with each component being the average of a variable in the analysis. The algorithm uses one initial observation per cluster as the mean for that cluster and then evaluates each of the remaining observations for inclusion into a cluster. As each observation is included, the mean of each cluster is recalculated and previously clustered observations are reevaluated for appropriate clustering. Observations and k number of means are reevaluated at each step until no further improvement can be achieved and all observations have been clustered. Conversely, a hierarchical clustering analysis (HCA) begins with one cluster per observation and then successively agglomerates clusters using an appropriate measure of similarity between clusters. As the number of observation per cluster is increased, the number of clusters is reduced until one cluster with all observations is reached. In HCA, a clustered observation is not reevaluated for a better cluster fit, but no supervision in the number of groups is required.

Answer 1

相比之下,钙组数据的相似集群试图减少变异性在每一个集群,提高变异性之间集群。kmeans方法的nonhierarchical监督分区CA(Rencher 2002、482、)。 集群的数量(k)是预定或监督一个向量指定一个集群的意思,或质心,每个组件是一个变量的平均对结果进行分析。该算法使用一个初始观察团簇的意思,并对每一个集群,其余的观察,融入打群架。当每一个观察,其中包括每个群集的意思是重新估算和先前群集观察为适当的新剧聚类。观察和k数量的方式是在每一步新剧,直到没有进一步的改进可以达到所有的观察已经群集。相反的,一个阶层式集群分析(混合信道分配策略)始于一个集群每观察,然后用一个合适的先后凝聚集群间的相似度衡量集群。为观察团簇的数量增加时,分组数减少到一个集群和所有的观察结论。在混合信道分配策略,一个群集结论并非为了更好的集群新剧吻合,但没有监督小组的数目是必需的。