Scalable robust clustering method for large and sparse data

Hämäläinen, Joonas; Kärkkäinen, Tommi; Rossi, Tuomo

dc.contributor.author	Hämäläinen, Joonas
dc.contributor.author	Kärkkäinen, Tommi
dc.contributor.author	Rossi, Tuomo
dc.date.accessioned	2019-02-11T11:21:55Z
dc.date.available	2019-02-11T11:21:55Z
dc.date.issued	2018
dc.identifier.citation	Hämäläinen, 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.other	CONVID_28889218
dc.identifier.other	TUTKAID_80472
dc.identifier.uri	https://jyx.jyu.fi/handle/123456789/62747
dc.description.abstract	Datasets 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.mimetype	application/pdf
dc.language.iso	eng
dc.publisher	ESANN
dc.relation.ispartof	ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.uri	https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-134.pdf
dc.rights	In Copyright
dc.subject.other	datasets
dc.subject.other	clustering
dc.title	Scalable robust clustering method for large and sparse data
dc.type	conferenceObject
dc.identifier.urn	URN:NBN:fi:jyu-201901281317
dc.contributor.laitos	Informaatioteknologian tiedekunta	fi
dc.contributor.laitos	Faculty of Information Technology	en
dc.contributor.oppiaine	Tietotekniikka	fi
dc.contributor.oppiaine	Mathematical Information Technology	en
dc.type.uri	http://purl.org/eprint/type/ConferencePaper
dc.date.updated	2019-01-28T07:15:15Z
dc.relation.isbn	978-2-87587-047-6
dc.type.coar	http://purl.org/coar/resource_type/c_5794
dc.description.reviewstatus	peerReviewed
dc.format.pagerange	449-454
dc.type.version	publishedVersion
dc.rights.copyright	© Authors, 2018
dc.rights.accesslevel	openAccess	fi
dc.relation.conference	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.relation.grantnumber	311877
dc.relation.grantnumber	315550
dc.subject.yso	data
dc.subject.yso	klusterianalyysi
dc.format.content	fulltext
jyx.subject.uri	http://www.yso.fi/onto/yso/p27250
jyx.subject.uri	http://www.yso.fi/onto/yso/p27558
dc.rights.url	http://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.funder	Suomen Akatemia	fi
dc.relation.funder	Suomen Akatemia	fi
dc.relation.funder	Academy of Finland	en
dc.relation.funder	Academy of Finland	en
jyx.fundingprogram	Profilointi, SA	fi
jyx.fundingprogram	Akatemiaohjelma, SA	fi
jyx.fundingprogram	Research profiles, AoF	en
jyx.fundingprogram	Academy Programme, AoF	en
jyx.fundinginformation	The work of TK has been supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI)
dc.type.okm	A4