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New technology management using time series regression and clustering

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dc.contributor.authorPark, S.S.-
dc.contributor.authorJun, S.-
dc.date.accessioned2021-09-07T04:24:07Z-
dc.date.available2021-09-07T04:24:07Z-
dc.date.created2021-06-17-
dc.date.issued2012-
dc.identifier.issn1738-9984-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/110732-
dc.description.abstractTechnology can be defined diversely according to the ways of its usage. In this paper, we define technology as a tool making new product and service using the developed results of science and engineering for improving the quality of human life. Technology management (TM) is important factor in the business planning of a company. Many companies have performed TM for developing new products and protecting their intellectual properties (IP). Patent is a typical IP, so we propose a TM approach using new patent analysis method. In this paper, we combine time series regression and clustering techniques. To assess the performance of our research, we will make experiment using the biotechnology patent data from the United State Patent and Trademark Office.-
dc.languageEnglish-
dc.language.isoen-
dc.titleNew technology management using time series regression and clustering-
dc.typeArticle-
dc.contributor.affiliatedAuthorPark, S.S.-
dc.identifier.scopusid2-s2.0-84866900467-
dc.identifier.bibliographicCitationInternational Journal of Software Engineering and its Applications, v.6, no.2, pp.155 - 160-
dc.relation.isPartOfInternational Journal of Software Engineering and its Applications-
dc.citation.titleInternational Journal of Software Engineering and its Applications-
dc.citation.volume6-
dc.citation.number2-
dc.citation.startPage155-
dc.citation.endPage160-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorClustering-
dc.subject.keywordAuthorPatent analysis-
dc.subject.keywordAuthorRegression analysis-
dc.subject.keywordAuthorTechnology management-
dc.subject.keywordAuthorTime series model-
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