Improvements and applications of the elements of prototype-based clustering

Hämäläinen, Joonas

dc.contributor.author	Hämäläinen, Joonas
dc.date.accessioned	2018-11-29T09:54:14Z
dc.date.available	2018-11-29T09:54:14Z
dc.date.issued	2018
dc.identifier.isbn	978-951-39-7621-7
dc.identifier.other	oai:jykdok.linneanet.fi:1908665
dc.identifier.uri	https://jyx.jyu.fi/handle/123456789/60387
dc.format.extent	1 verkkoaineisto (58 sivua, 55 sivua useina numerointijaksoina, 18 numeroimatonta sivua) : kuvitettu
dc.format.mimetype	application/pdf
dc.language.iso	eng
dc.publisher	Jyväskylän yliopisto
dc.relation.ispartofseries	JYU dissertations
dc.relation.haspart	<b>Artikkeli I:</b> Hämäläinen, J., & Kärkkäinen, T. (2016). Initialization of Big Data Clustering using Distributionally Balanced Folding. In <i>ESANN 2016 : Proceedings of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 587-592). ESANN.</i> <a href="https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-93.pdf"target="_blank"> https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-93.pdf</a>
dc.relation.haspart	<b>Artikkeli II:</b> Joonas Hämäläinen, Tommi Kärkkäinen and Tuomo Rossi. Scalable initial-ization methods for clustering large datasets. <i>Pattern Recognition Letters (in revision), 2018</i>
dc.relation.haspart	<b>Artikkeli III:</b> 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 (pp. 449-454). </i> <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.relation.haspart	<b>Artikkeli IV:</b> Hämäläinen, J., Jauhiainen, S., & Kärkkäinen, T. (2017). Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering. <i>Algorithms, 10 (3), 105.</i> <a href="http://dx.doi.org/10.3390/a10030105"target="_blank"> DOI: 10.3390/a10030105</a>
dc.relation.haspart	<b>Artikkeli V:</b> Saarela, M., Hämäläinen, J., & Kärkkäinen, T. (2017). Feature Ranking of Large, Robust, and Weighted Clustering Result. In <i>K. Jinho, S. Kyuseok, C. Longbing, L. Jae-Gil, L. Xuemin, & M. Yang-Sae (Eds.), Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I (pp. 96-109). Springer International Publishing.</i> <a href="http://dx.doi.org/10.1007/978-3-319-57454-7_8"target="_blank"> DOI: 10.1007/978-3-319-57454-7_8</a>
dc.relation.haspart	<b>Artikkeli VI:</b> Joonas Hämäläinen, Tommi Kärkkäinen and João P. P. Gomes. Clustering-Based Reference Points Selection for the Minimal Learning Machine. <i>Manuscript, 2018.</i>
dc.rights	In Copyright
dc.subject.other	knowledge discovery
dc.subject.other	data mining
dc.subject.other	machine learning
dc.subject.other	prototype-based clustering
dc.subject.other	big data
dc.subject.other	parallel computing
dc.subject.other	robust clustering
dc.subject.other	clustering initialization
dc.subject.other	K-means
dc.subject.other	minimal learning machine
dc.subject.other	random projection
dc.title	Improvements and applications of the elements of prototype-based clustering
dc.type	Diss.
dc.identifier.urn	URN:ISBN:978-951-39-7621-7
dc.contributor.yliopisto	University of Jyväskylä	en
dc.contributor.yliopisto	Jyväskylän yliopisto	fi
dc.contributor.oppiaine	Tietotekniikka	fi
dc.relation.issn	2489-9003
dc.relation.numberinseries	43
dc.rights.copyright	© The Author & University of Jyväskylä
dc.rights.accesslevel	openAccess
dc.type.publication	doctoralThesis
dc.subject.yso	tiedonlouhinta
dc.subject.yso	koneoppiminen
dc.subject.yso	big data
dc.subject.yso	klusterianalyysi
dc.subject.yso	rinnakkaiskäsittely
dc.format.content	fulltext
dc.rights.url	http://rightsstatements.org/page/InC/1.0/?language=en

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