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dc.contributor.authorCordeiro De Amorim, Renato
dc.contributor.authorFenner, Trevor
dc.date.accessioned2016-04-04T11:47:11Z
dc.date.available2016-04-04T11:47:11Z
dc.date.issued2012
dc.identifier.citationCordeiro De Amorim , R & Fenner , T 2012 , Weighting Features for Partition around Medoids Using the Minkowski Metric . in Advances in Intelligent Data Analysis XI . Lecture Notes in Computer Science , vol. 7619 , Springer , pp. 35-44 , 11th Int Symposium, IDA 2012 , Helsinki , Finland , 25/10/12 . https://doi.org/10.1007/978-3-642-34156-4_5
dc.identifier.citationconference
dc.identifier.isbn978-3-642-34155-7
dc.identifier.isbn978-3-642-34156-4
dc.identifier.otherPURE: 9822454
dc.identifier.otherPURE UUID: f75fa449-7a02-4282-999d-a8934938a831
dc.identifier.otherScopus: 84868015386
dc.identifier.urihttp://hdl.handle.net/2299/16911
dc.description.abstractIn this paper we introduce the Minkowski weighted partition around medoids algorithm (MW-PAM). This extends the popular partition around medoids algorithm (PAM) by automatically assigning K weights to each feature in a dataset, where K is the number of clusters. Our approach utilizes the within-cluster variance of features to calculate the weights and uses the Minkowski metric. We show through many experiments that MW-PAM, particularly when initialized with the Build algorithm (also using the Minkowski metric), is superior to other medoid-based algorithms in terms of both accuracy and identification of irrelevant features.en
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofAdvances in Intelligent Data Analysis XI
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.titleWeighting Features for Partition around Medoids Using the Minkowski Metricen
dc.contributor.institutionSchool of Computer Science
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2012
rioxxterms.versionofrecordhttps://doi.org/10.1007/978-3-642-34156-4_5
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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