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Evaluation of data clustering for stepwise linear regression on use case points estimation

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dc.title Evaluation of data clustering for stepwise linear regression on use case points estimation en
dc.contributor.author Šilhavý, Petr
dc.contributor.author Šilhavý, Radek
dc.contributor.author Prokopová, Zdenka
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783319571409
dc.date.issued 2017
utb.relation.volume 575
dc.citation.spage 491
dc.citation.epage 496
dc.event.title 6th Computer Science On-line Conference, CSOC 2017
dc.event.sdate 2017-04-26
dc.event.edate 2017-04-29
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-57141-6_52
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-57141-6_52
dc.subject Clustering en
dc.subject Effort estimation en
dc.subject Parametric model en
dc.subject Stepwise linear regression en
dc.subject Use case points en
dc.description.abstract In this paper, stepwise linear regression model in conjunction with clustering for effort estimation is investigated. Effect of clustering is compared to Use Case Points model. The 2 to 20 clusters were tested. As shown increasing a number of clusters brings lower prediction errors. More clusters lower a distance between clusters members, which allows to construct more capable stepwise linear regression model. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007392
utb.identifier.scopus 2-s2.0-85018693427
utb.identifier.wok 000405338500052
utb.source d-scopus
dc.date.accessioned 2017-09-08T12:14:50Z
dc.date.available 2017-09-08T12:14:50Z
utb.contributor.internalauthor Šilhavý, Petr
utb.contributor.internalauthor Šilhavý, Radek
utb.contributor.internalauthor Prokopová, Zdenka
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