Feature Ranking of Large, Robust, and Weighted Clustering Result
Saarela, 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.), 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. Lecture Notes in Computer Science, 10234. https://doi.org/10.1007/978-3-319-57454-7_8
Julkaistu sarjassa
Lecture Notes in Computer ScienceToimittajat
Päivämäärä
2017Tekijänoikeudet
© 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.
A 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].
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Julkaisija
Springer International PublishingEmojulkaisun ISBN
978-3-319-57453-0Konferenssi
Pacific-Asia Conference on Knowledge Discovery and Data MiningKuuluu julkaisuun
Advances in Knowledge Discovery and Data Mining : 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part IISSN Hae Julkaisufoorumista
0302-9743Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/26981996
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