Show simple item record

dc.contributor.authorHämäläinen, Joonas
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.authorRossi, Tuomo
dc.date.accessioned2019-02-11T11:21:55Z
dc.date.available2019-02-11T11:21:55Z
dc.date.issued2018
dc.identifier.citationHämäläinen, J., Kärkkäinen, T., & Rossi, T. (2018). Scalable robust clustering method for large and sparse data. In <i>ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</i> (pp. 449-454). ESANN. <a href="https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf" target="_blank">https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf</a>
dc.identifier.otherCONVID_28889218
dc.identifier.otherTUTKAID_80472
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/62747
dc.description.abstractDatasets for unsupervised clustering can be large and sparse, with significant portion of missing values. We present here a scalable version of a robust clustering method with the available data strategy. Moreprecisely, a general algorithm is described and the accuracy and scalability of a distributed implementation of the algorithm is tested. The obtained results allow us to conclude the viability of the proposed approach.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherESANN
dc.relation.ispartofESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.urihttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf
dc.rightsIn Copyright
dc.subject.otherdatasets
dc.subject.otherclustering
dc.titleScalable robust clustering method for large and sparse data
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201901281317
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-01-28T07:15:15Z
dc.relation.isbn978-2-87587-047-6
dc.description.reviewstatuspeerReviewed
dc.format.pagerange449-454
dc.type.versionpublishedVersion
dc.rights.copyright© Authors, 2018
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.grantnumber311877
dc.relation.grantnumber315550
dc.subject.ysodata
dc.subject.ysoklusterianalyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p27250
jyx.subject.urihttp://www.yso.fi/onto/yso/p27558
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundinginformationThe work of TK has been supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

In Copyright
Except where otherwise noted, this item's license is described as In Copyright