| DC 欄位 |
值 |
語言 |
| DC.contributor | 統計研究所 | zh_TW |
| DC.creator | 范文翔 | zh_TW |
| DC.creator | Wen-Hsiang Fan | en_US |
| dc.date.accessioned | 2008-7-17T07:39:07Z | |
| dc.date.available | 2008-7-17T07:39:07Z | |
| dc.date.issued | 2008 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=952205019 | |
| dc.contributor.department | 統計研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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. | en_US |
| DC.subject | K平均值分群演算法 | zh_TW |
| DC.subject | 訊息準則 | zh_TW |
| DC.subject | Information criterion | en_US |
| DC.subject | K-means clustering algorithm | en_US |
| DC.title | 一個估計資料群數的新方法 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | A new method for estimating the number of clusters | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |