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dc.contributor.authorNiemelä, Marko
dc.contributor.authorÄyrämö, Sami
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2019-02-11T11:11:32Z
dc.date.available2019-02-11T11:11:32Z
dc.date.issued2018fi
dc.identifier.citationNiemelä, M., Äyrämö, S., & Kärkkäinen, T. (2018). Comparison of cluster validation indices with missing data. In <em>ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</em> (pp. 461-466). ESANN. Retrieved from <a href="https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-16.pdf">https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-16.pdf</a>fi
dc.identifier.otherTUTKAID_80473
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/62745
dc.description.abstractClustering is an unsupervised machine learning technique, which aims to divide a given set of data into subsets. The number of hidden groups in cluster analysis is not always obvious and, for this purpose, various cluster validation indices have been suggested. Recently some studies reviewing validation indices have been provided, but any experiments against missing data are not yet available. In this paper, performance of ten well-known indices on ten synthetic data sets with various ratios of missing values is measured using squared euclidean and city block distances based clustering. The original indices are modified for a city block distance in a novel way. Experiments illustrate the different degree of stability for the indices with respect to the missing data.fi
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/es2018-16.pdf
dc.rightsIn Copyright
dc.subject.otherdatafi
dc.subject.otherklusterianalyysifi
dc.subject.otherclusteringfi
dc.subject.othercluster validationfi
dc.titleComparison of cluster validation indices with missing datafi
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201901281318
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2019-01-28T07:15:17Z
dc.relation.isbn978-287587047-6
dc.description.reviewstatuspeerReviewed
dc.format.pagerange461-466
dc.type.versionPublisher's PDF
dc.rights.copyright© Authors, 2018
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en


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