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Title:	以資料採礦方法探討國內數位落差之現象 Effect of Digital Divide in Taiwan: Data Mining Applications
Authors:	林建宇 Lin,chien yu
Contributors:	鄭宇庭林建宇 Lin,chien yu
Keywords:	數位落差資訊富人資訊窮人資料採礦分類迴歸樹 C5.0決策樹 C&RT分類樹 CHAID分類樹 Digital Divide Information-rich Information-poor Data Mining Decision Tree C5.0 C&RT CHAID
Date:	2009
Issue Date:	2010-12-08 01:51:57 (UTC+8)
Abstract:	全球化時代與資訊化社會的來臨，電腦與網際網路成為生活中不可或缺的要素，儘管至2008年為止，我國有將近七成的民眾透過網路科技享受到更多的便利性，但社會上仍存在著數位落差（digital divide）的問題，數位落差除了使得資訊窮人（information-poor）不易取得資訊，亦將對其經濟、人權等各方面造成影響。故研究目的在利用資料採礦的應用，配合SPSS Clementine 12.0的軟體，探討數位落差的現象，並嘗試找出形成數位落差的影響原因。　　本研究主要投入人口統計變數以及生活型態變數，並藉由C5.0決策樹、C&RT分類樹，以及CHAID分類樹建立模型，透過這三個分類迴歸樹的模型，發現到「年齡」、「教育程度」、「地理區域」、「個人資產狀況」、「經濟主要來源：子女」、「個人每月可支配所得」以及「收入來源：薪資」共七項變數同時對民眾是否成為數位落差中的資訊富人（information-rich）有著較重要的影響性，因此，研究最後依據此七項進行政策建議，以提供相關單位之參考。　　In this globalized and informational society, computers and internet networks are essential elements in our daily lives. Until the year 2008, almost 70% of population in Taiwan has enjoyed greater conveniences through networking technologies. However, the issue of “digital divide” remains, where information-poor cannot obtain information easily, and the issue affects the society in terms of economies and human rights. Consequently, the purpose of this research is aimed to find the reasons behind “digital divide” using data-mining techniques with SPSS Clementine 12.0 statistical software. 　　The research will input demographic variables and life-style variables. Using C5.0 decision tree, C&RT tree, and CHAID methodologies to build model, and subsequently discovers that whether the 7 variables - “age”, “level of education”, “location”, “personal asset status”, “main source of income: children”, “monthly personal disposal income” and “source of income: salary” will have significant impacts on information-rich population within “digital divide”. Therefore, the research recommendations will be provided according to the results from these 7 variables.
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Description:	碩士國立政治大學企業管理研究所 96355064 98
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0096355064
Data Type:	thesis
Appears in Collections:	[Department of Business Administation] Theses

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