A study on the comparison between two approached on fashion trend analysis

dc.contributor.author an, hyosun
dc.contributor.author park, minjung
dc.date 2018-12-13T09:01:20.000
dc.date.accessioned 2020-06-30T05:43:40Z
dc.date.available 2020-06-30T05:43:40Z
dc.date.issued 2018-01-01
dc.description.abstract <p>The fashion industry in the 4<sup>th</sup> industrial revolution era is shifting to a paradigm that predicts and responds to consumer demands. Big data technologies are especially receiving an increasing amount of attention in the field of fashion design. Massive user data accumulation allows designers to make more accurate predictions for the latest seasonal fashion trends. A recent study on fashion trend analysis through IT technology was published in 2015 (Lin et al., 2015). However, since the study was accomplished in the field of computer information, the results of data analysis are derived in a biased way. The purpose of this study is to examine traditional trend analysis methods and big data analysis methods in both domestic and overseas’ fashion studies and also to propose a research method for analyzing user-oriented information on fashion design trends to supplement the limitations.</p>
dc.identifier archive/lib.dr.iastate.edu/itaa_proceedings/2018/presentations/63/
dc.identifier.articleid 3206
dc.identifier.contextkey 13334897
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath itaa_proceedings/2018/presentations/63
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/52247
dc.relation.ispartofseries International Textile and Apparel Association (ITAA) Annual Conference Proceedings
dc.source.bitstream archive/lib.dr.iastate.edu/itaa_proceedings/2018/presentations/63/2018ITAAKSCT_O05_AnPark.docx|||Sat Jan 15 01:20:16 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/itaa_proceedings/2018/presentations/63/auto_convert.pdf|||Sat Jan 15 01:20:17 UTC 2022
dc.subject.disciplines Fashion Design
dc.title A study on the comparison between two approached on fashion trend analysis
dc.type event
dc.type.genre oral
dspace.entity.type Publication
relation.isSeriesOfPublication 5d0f3f8c-2190-47b2-bb58-b59e2d1740d5
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