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題名 Trend analysis of machine learning -A text mining and document clustering methodology
作者 Yang, JiannMin;Liao, W.-C.
楊建民
貢獻者 資訊管理學系
關鍵詞 Application domains; Automatic clustering; Document clustering; Input variables; Machine-learning; Operation efficiencies; Research areas; Self-organizations; Social science citation indices; Term Frequency; Text mining; Trend analysis; Cluster analysis; Education; Glossaries; Information retrieval; Mining; Neural networks; Robot learning; Research
日期 2009
上傳時間 16-十月-2015 15:33:57 (UTC+8)
摘要 The Machine Learning is certificated as one of the most important technologies in today`s world. There are several various researches applying Machine Learning to improve its operation efficiency in many different aspects. Based on the Social Science Citation Index (SSCI) database, this research is using text mining technology which collecting the homogeneous glossaries in the articles, conducting to the literature cluster analysis. To select the term frequency index which generated by various glossaries aggregation from each article as well as an input variable for Self-Organization map (SOM) network, following by utilizing the network neuron automatic clustering function, dividing into 10 application domains of machine learning, finally proceeding the trend analysis coordinated with the articles by published year, discovering the historical vein and collecting the results by each research area, and further forecasting the future possible tendency. © 2009 IEEE.
關聯 Proceedings - 2009 International Conference on New Trends in Information and Service Science, NISS 2009,481-485
資料類型 conference
DOI http://dx.doi.org/10.1109/NISS.2009.176
dc.contributor 資訊管理學系
dc.creator (作者) Yang, JiannMin;Liao, W.-C.
dc.creator (作者) 楊建民zh_TW
dc.date (日期) 2009
dc.date.accessioned 16-十月-2015 15:33:57 (UTC+8)-
dc.date.available 16-十月-2015 15:33:57 (UTC+8)-
dc.date.issued (上傳時間) 16-十月-2015 15:33:57 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/78976-
dc.description.abstract (摘要) The Machine Learning is certificated as one of the most important technologies in today`s world. There are several various researches applying Machine Learning to improve its operation efficiency in many different aspects. Based on the Social Science Citation Index (SSCI) database, this research is using text mining technology which collecting the homogeneous glossaries in the articles, conducting to the literature cluster analysis. To select the term frequency index which generated by various glossaries aggregation from each article as well as an input variable for Self-Organization map (SOM) network, following by utilizing the network neuron automatic clustering function, dividing into 10 application domains of machine learning, finally proceeding the trend analysis coordinated with the articles by published year, discovering the historical vein and collecting the results by each research area, and further forecasting the future possible tendency. © 2009 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - 2009 International Conference on New Trends in Information and Service Science, NISS 2009,481-485
dc.subject (關鍵詞) Application domains; Automatic clustering; Document clustering; Input variables; Machine-learning; Operation efficiencies; Research areas; Self-organizations; Social science citation indices; Term Frequency; Text mining; Trend analysis; Cluster analysis; Education; Glossaries; Information retrieval; Mining; Neural networks; Robot learning; Research
dc.title (題名) Trend analysis of machine learning -A text mining and document clustering methodology
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/NISS.2009.176
dc.doi.uri (DOI) http://dx.doi.org/10.1109/NISS.2009.176