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dc.contributor.authorSaarela, Mirka
dc.contributor.authorHämäläinen, Joonas
dc.contributor.authorKärkkäinen, Tommi
dc.contributor.editorJinho, Kim
dc.contributor.editorKyuseok, Shim
dc.contributor.editorLongbing, Cao
dc.contributor.editorJae-Gil, Lee
dc.contributor.editorXuemin, Lin
dc.contributor.editorYang-Sae, Moon
dc.date.accessioned2017-05-11T10:15:43Z
dc.date.available2017-05-11T10:15:43Z
dc.date.issued2017
dc.identifier.citationSaarela, M., Hämäläinen, J., & Kärkkäinen, T. (2017). Feature Ranking of Large, Robust, and Weighted Clustering Result. In K. Jinho, S. Kyuseok, C. Longbing, L. Jae-Gil, L. Xuemin, & M. Yang-Sae (Eds.), <i>Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I</i> (pp. 96-109). Springer International Publishing. Lecture Notes in Computer Science, 10234. <a href="https://doi.org/10.1007/978-3-319-57454-7_8" target="_blank">https://doi.org/10.1007/978-3-319-57454-7_8</a>
dc.identifier.otherCONVID_26981996
dc.identifier.otherTUTKAID_73663
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/53899
dc.description.abstractA clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking method by generalizing the computation of the Kruskal-Wallis H test statistic from sample to population level with two different approaches. Application of these enlargements to both the input variables used in clustering and to metadata provides automatic determination of variable ranking that can be used to explain and distinguish the groups of population. The ranking method is illustrated with an open data and then, applied to advance the educational knowledge discovery from large scale international student assessment data, whose robust clustering into disjoint groups on three different levels of abstraction was performed in [19].
dc.format.extent778
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofAdvances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.otherpopulation analysis
dc.subject.otherKruskal-Wallis test
dc.subject.otherrobust clustering
dc.subject.othereducational knowledge discovery
dc.titleFeature Ranking of Large, Robust, and Weighted Clustering Result
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201705022143
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.updated2017-05-02T12:15:06Z
dc.relation.isbn978-3-319-57453-0
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange96-109
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferencePacific-Asia Conference on Knowledge Discovery and Data Mining
dc.relation.doi10.1007/978-3-319-57454-7_8
dc.type.okmA4


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