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題名 應用增長層級式自我組織映射圖於歷年研究主題圖之呈現
其他題名 Multi-Layer Visual Presentation of Annual Research Topics Using Growing Hierarchical Self-Organizing Map
作者 姜國輝
楊喻翔
Chiang, Johannes K;Yang, Yu-Xiang
貢獻者 資管系
關鍵詞 增長層級式自我組織映射圖 ; 主題圖 ; 利他 ; 歷年
Growing Hierarchical Self-Organizing Map (GHSOM) ; Topic map ; Altruism ; Annual
日期 2012-06
上傳時間 21-Feb-2014 10:57:40 (UTC+8)
摘要 本研究以增長層級式自我組織映射圖(Growing Hierarchical Self-Organizing Map, GHSOM)分析出利他研究文獻中重要的歷年研究主題及概念交涉關係。本研究除了將年度的因素考慮主題分析之外,利用GHSOM除了提供之前自我組織映射圖(Self-Organizing Map, SOM)能重要研究主題上的論文分布情形與主題彼此之間的視覺化,它更改進了SOM的二個缺點,無法自動確定大小的映射圖及表達出資料之間的階層性。本研究利用利他相關文獻作範例,分析結果除了提供這些學科的所關注的主題及學科之間交互關係,有助我們快速掌握研究文獻所呈現的研究概況。
The purpose of this study was to propose a hierarchical, annual research topic maps using Growing Hierarchical Self-Organizing Map (GHSOM), an improved Self-Organizing Map (SOM) algorithm. Unlike SOM, GHSOM can implement a dynamic architecture automatically and represents the hierarchical relations of results. The topic map illustrated the delicate intertwining of topics of annual research and provided a more explicit illustration of the concepts within each subject area. After taking up one example of altruism, this study suggests that topic map may disclose some important annual research topics from a whole bunch of data.
關聯 圖書資訊學研究, 6(2), 1-35
資料來源 http://www.airitilibrary.com/Publication/alDetailedMesh?DocID=19909128-201206-201207310018-201207310018-1-35
資料類型 article
dc.contributor 資管系en_US
dc.creator (作者) 姜國輝zh_TW
dc.creator (作者) 楊喻翔zh_TW
dc.creator (作者) Chiang, Johannes K;Yang, Yu-Xiang-
dc.date (日期) 2012-06en_US
dc.date.accessioned 21-Feb-2014 10:57:40 (UTC+8)-
dc.date.available 21-Feb-2014 10:57:40 (UTC+8)-
dc.date.issued (上傳時間) 21-Feb-2014 10:57:40 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64064-
dc.description.abstract (摘要) 本研究以增長層級式自我組織映射圖(Growing Hierarchical Self-Organizing Map, GHSOM)分析出利他研究文獻中重要的歷年研究主題及概念交涉關係。本研究除了將年度的因素考慮主題分析之外,利用GHSOM除了提供之前自我組織映射圖(Self-Organizing Map, SOM)能重要研究主題上的論文分布情形與主題彼此之間的視覺化,它更改進了SOM的二個缺點,無法自動確定大小的映射圖及表達出資料之間的階層性。本研究利用利他相關文獻作範例,分析結果除了提供這些學科的所關注的主題及學科之間交互關係,有助我們快速掌握研究文獻所呈現的研究概況。en_US
dc.description.abstract (摘要) The purpose of this study was to propose a hierarchical, annual research topic maps using Growing Hierarchical Self-Organizing Map (GHSOM), an improved Self-Organizing Map (SOM) algorithm. Unlike SOM, GHSOM can implement a dynamic architecture automatically and represents the hierarchical relations of results. The topic map illustrated the delicate intertwining of topics of annual research and provided a more explicit illustration of the concepts within each subject area. After taking up one example of altruism, this study suggests that topic map may disclose some important annual research topics from a whole bunch of data.en_US
dc.format.extent 3493694 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) 圖書資訊學研究, 6(2), 1-35en_US
dc.source.uri (資料來源) http://www.airitilibrary.com/Publication/alDetailedMesh?DocID=19909128-201206-201207310018-201207310018-1-35en_US
dc.subject (關鍵詞) 增長層級式自我組織映射圖 ; 主題圖 ; 利他 ; 歷年en_US
dc.subject (關鍵詞) Growing Hierarchical Self-Organizing Map (GHSOM) ; Topic map ; Altruism ; Annualen_US
dc.title (題名) 應用增長層級式自我組織映射圖於歷年研究主題圖之呈現zh_TW
dc.title.alternative (其他題名) Multi-Layer Visual Presentation of Annual Research Topics Using Growing Hierarchical Self-Organizing Map-
dc.type (資料類型) articleen