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| Title | Machine learning trend anticipation by text mining methodology based on SSCI database |
| Creator | Chiang, Johannes Kuo-huie;Liao, W.-C. 姜國輝 |
| Contributor | 資訊管理學系 |
| Key Words | Machine-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 |
| Date | 2009 |
| Date Issued | 7-May-2015 17:40:25 (UTC+8) |
| Summary | This 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. |
| Relation | NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC,612-617 |
| Type | conference |
| DOI | http://dx.doi.org/10.1109/NCM.2009.382 |
| dc.contributor | 資訊管理學系 | |
| dc.creator (作者) | Chiang, Johannes Kuo-huie;Liao, W.-C. | |
| dc.creator (作者) | 姜國輝 | zh_TW |
| dc.date (日期) | 2009 | |
| dc.date.accessioned | 7-May-2015 17:40:25 (UTC+8) | - |
| dc.date.available | 7-May-2015 17:40:25 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-May-2015 17:40:25 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75046 | - |
| dc.description.abstract (摘要) | This 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.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC,612-617 | |
| dc.subject (關鍵詞) | Machine-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.title (題名) | Machine learning trend anticipation by text mining methodology based on SSCI database | |
| dc.type (資料類型) | conference | en |
| dc.identifier.doi (DOI) | 10.1109/NCM.2009.382 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1109/NCM.2009.382 |