Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/113055
DC FieldValueLanguage
dc.contributor資管系zh_TW
dc.creator曾淑峰zh_TW
dc.creator楊建民zh_TW
dc.creatorTseng, Shu Fengen_US
dc.creatorWon, Yu Lingen_US
dc.creatorYang, Jiann Minen_US
dc.date2016
dc.date.accessioned2017-09-15T08:09:47Z-
dc.date.available2017-09-15T08:09:47Z-
dc.date.issued2017-09-15T08:09:47Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/113055-
dc.description.abstractAlong with more and faster accumulation of electronic business data, Data Mining and the newer Big Data issues are attracting more attention. This paper reports the literature analysis based on the publication journals and articles in the research databases. The ranking comparisons of top 10 article counts in 2014 on Data Mining and Big Data show that there are 9 in common in the top 10 author countries but only 2 in common in the top 10 author organisations. There are 6 in common in the top 10 research areas but only 2 in common in the top 10 journal names. However, near 1/3 authors contributing to the Big Data literature come from the pool of authors who have publications in the Data Mining subject. Hopefully, their Big Data research in the value dimension may link better to the Data Mining knowledge and methodologies.en_US
dc.format.extent208 bytes-
dc.format.mimetypetext/html-
dc.relationInternational Journal of Electronic Business, 13(1), 38-69en_US
dc.titleA bibliometric analysis on data mining and big dataen_US
dc.typearticle
dc.identifier.doi10.1504/IJEB.2016.075333
dc.doi.urihttp://dx.doi.org/10.1504/IJEB.2016.075333
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
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