DocumentCode :
3462944
Title :
Trend Analysis of Machine Learning - A Text Mining And Document Clustering Methodology
Author :
Yang, Jiann-Min ; Liao, Wei-Cheng ; Wu, Wen-Chin ; Yin, Chi-Yen
Author_Institution :
Dept. of Manage. Inf. Syst., Nat. Chengchi Univ., Taipei, Taiwan
fYear :
2009
fDate :
June 30 2009-July 2 2009
Firstpage :
481
Lastpage :
486
Abstract :
The machine learning is certificated as one of the most important technologies in todaypsilas 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.
Keywords :
data mining; learning (artificial intelligence); neural nets; pattern clustering; self-organising feature maps; text analysis; document clustering; machine learning; network neuron automatic clustering function; self-organization map network; social science citation index; term frequency index; text mining; trend analysis; Citation analysis; Databases; Frequency conversion; Indexes; Input variables; Machine learning; Neurons; Terminology; Text mining; Veins; document clustering; neural network; text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
New Trends in Information and Service Science, 2009. NISS '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-3687-3
Type :
conf
DOI :
10.1109/NISS.2009.176
Filename :
5260843
Link To Document :
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