Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/78976
DC FieldValueLanguage
dc.contributor資訊管理學系
dc.creatorYang, JiannMin;Liao, W.-C.
dc.creator楊建民zh_TW
dc.date2009
dc.date.accessioned2015-10-16T07:33:57Z-
dc.date.available2015-10-16T07:33:57Z-
dc.date.issued2015-10-16T07:33:57Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/78976-
dc.description.abstractThe Machine Learning is certificated as one of the most important technologies in today`s 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. © 2009 IEEE.
dc.format.extent176 bytes-
dc.format.mimetypetext/html-
dc.relationProceedings - 2009 International Conference on New Trends in Information and Service Science, NISS 2009,481-485
dc.subjectApplication domains; Automatic clustering; Document clustering; Input variables; Machine-learning; Operation efficiencies; Research areas; Self-organizations; Social science citation indices; Term Frequency; Text mining; Trend analysis; Cluster analysis; Education; Glossaries; Information retrieval; Mining; Neural networks; Robot learning; Research
dc.titleTrend analysis of machine learning -A text mining and document clustering methodology
dc.typeconferenceen
dc.identifier.doi10.1109/NISS.2009.176
dc.doi.urihttp://dx.doi.org/10.1109/NISS.2009.176
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.openairetypeconference-
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