估計資料群數是群集分析(cluster analysis)中一個重要的問題。在本篇論文中，我們嘗試模型選取中最被普遍使用的貝氏訊息準則(Bayesian information criterion)做為群集問題中選取群數的標準。然而，在資料變數為一維的情況下，我們發現使用BIC會高估資料的真實群數；即使嘗試各種不同的懲罰項，並沒有找到一個有效的一致性訊息準則(consistent information criterion)。因此，本篇論文提出了一個群數估計的新方法，並經由程式模擬說明其估計資料群數的準確性。 A major problem in cluster analysis is to find the number of clusters. In this paper, we try to use Bayesian information criterion(BIC), a wide-used criterion in model selection problem, as a criterion to estimate the number of clusters. However, we found that the ture number of clusters would be overestimated when using BIC as a criterion in one dimension case. We can not find a consistent information criterion in the problem of number estimation. We propose a new method for estimating the number of clusters and show the currency of the method via simulation study.
