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Title: Machine learning trend anticipation by text mining methodology based on SSCI database
Authors: Chiang, Johannes Kuo-huie;Liao, W.-C.
Contributors: 資訊管理學系
Keywords: 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
Issue Date: 2015-05-07 17:40:25 (UTC+8)
Abstract: 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
Data Type: conference
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