关於群集分析，我需要根据6个变项分数将手上的资料分作数个群集 目前先以SPSS跑二阶段 已用Ward’和Euclidean distance跑出树状图，可以看出分成4~5个群集较适宜 但在跑k-means之前，我要怎麽决定是分成4个或5个最佳？ (参考文献的资料是分成4~9个，最後觉得6个最佳，但我不懂的是6是怎麽出来的！) 参考文献的做法是跑Cohen’s kappa 是指初始中心点和最终中心点的2种分群方式去跑吗？(还是我对文献理解错误?) 觉得很疑惑，为什麽可以这样跑？ 一般都是该如何决定最佳集群数呢？ 补充-- 文献是这样描述的： This procedure begins by randomly assigning the sample into two groups. The cluster centers of each group from the first step are used as initial cluster centers for a series of k-means analyses that assign participants to clusters ranging from four to nine. Then, another set of k-means analyses are computed for each group, but in this case, the cluster centers from the opposite group are used to assign participants to the clusters. The two sets of k-means yield two sets of cluster assignmentsper group. The sets within a group are then compared via Cohen’s kappa (Cohen, 1960) to determine the reliability of cluster assignment, or in other words, the degree to which participants in each group are assigned to the same cluster given different initial cluster centers. 感谢指点！ --

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