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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/7724

    Title: 一個估計資料群數的新方法;A new method for estimating the number of clusters
    Authors: 范文翔;Wen-Hsiang Fan
    Contributors: 統計研究所
    Keywords: K平均值分群演算法;訊息準則;Information criterion;K-means clustering algorithm
    Date: 2008-07-07
    Issue Date: 2009-09-22 11:03:12 (UTC+8)
    Publisher: 國立中央大學圖書館
    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。 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.
    Appears in Collections:[統計研究所] 博碩士論文

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