Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/75046
DC FieldValueLanguage
dc.contributor資訊管理學系
dc.creatorChiang, Johannes Kuo-huie;Liao, W.-C.
dc.creator姜國輝zh_TW
dc.date2009
dc.date.accessioned2015-05-07T09:40:25Z-
dc.date.available2015-05-07T09:40:25Z-
dc.date.issued2015-05-07T09:40:25Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/75046-
dc.description.abstractThis paper is providing an introduction to the text mining methodology. There are many different researches which applying machine learning to improve its management application efficiency in various domains. This research is utilizing text mining technology, including "two step autoclustering", "glossaries aggregation", "TF-IDF" and so on, which collecting the homogeneous glossaries from articles, guiding to the literature cluster analysis based on the Social Science Citation Index (SSCI) database. The result discovered that the research domains of artificial intelligence, document pattern and financial related are the most prosperous fields on machine learning application, It is leading by information technology development progressing, web 2.0 is also a boost to research morale. All of these will become a power for important developing direction on machine learning in near future. © 2009 IEEE.
dc.format.extent176 bytes-
dc.format.mimetypetext/html-
dc.relationNCM 2009 - 5th International Joint Conference on INC, IMS, and IDC,612-617
dc.subjectMachine-learning; Management applications; On-machines; Research domains; Social science citation indices; Text mining; Web 2.0; Artificial intelligence; Cluster analysis; Glossaries; Learning systems; Research; Semiconductor storage; Technological forecasting; Information management
dc.titleMachine learning trend anticipation by text mining methodology based on SSCI database
dc.typeconferenceen
dc.identifier.doi10.1109/NCM.2009.382
dc.doi.urihttp://dx.doi.org/10.1109/NCM.2009.382
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
item.openairetypeconference-
item.cerifentitytypePublications-
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