DocumentCode :
2366973
Title :
Machine Learning Trend Anticipation by Text Mining Methodology Based on SSCI Database
Author :
Chiang, Johannes K. ; Wu, Wen-Chin ; Liao, Wei-Cheng ; Yin, Chi-Yen
Author_Institution :
Dept. of Manage. Inf. Syst., Nat. Chengchi Univ., Taipei, Taiwan
fYear :
2009
fDate :
25-27 Aug. 2009
Firstpage :
612
Lastpage :
617
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 auto-clustering", "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.
Keywords :
Internet; citation analysis; data mining; glossaries; learning (artificial intelligence); pattern clustering; text analysis; SSCI database; Social Science Citation Index; TF-IDF; Web 2.0; artificial intelligence; document pattern; financial domain; glossaries aggregation; homogeneous glossaries; information technology development; literature cluster analysis; machine learning trend anticipation; management application efficiency; text mining methodology; two step auto-clustering; Clustering algorithms; Clustering methods; Databases; Documentation; Machine learning; Management information systems; Technology management; Terminology; Text mining; Visualization; Machine Learnin; Text Mining; Two step auto-clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
INC, IMS and IDC, 2009. NCM '09. Fifth International Joint Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-5209-5
Electronic_ISBN :
978-0-7695-3769-6
Type :
conf
DOI :
10.1109/NCM.2009.382
Filename :
5331782
Link To Document :
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