<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/CDD0359F-7088-42EC-A4C5-1873F2339420"><efrbr-work:titleOfTheWork>Clustering healthcare processes with a robust approach
</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/CDD0359F-7088-42EC-A4C5-1873F2339420"><efrbr-expression:titleOfTheExpression>Clustering healthcare processes with a robust approach
</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Πλήρης Δημοσίευση σε Συνέδριο
            Conference Full Paper
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2015-11-04</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2013</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>Process Mining has been proved to be an effective approach to discover process models from event logs. However, when a highly diversified behavior is allowed (like in a healthcare environment) mining a single model could yield pointless results. One way to tackle this diversity is clustering the log into coherent groups. In this work we propose a robust spectral clustering approach. The spectral approach let one with greater flexibility in representing data (a similarity measure is enough) while the proposed robust technique can handle outliers, a common occurrence in healthcare processes. </efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="conference name">26th European Conference on Operational Research</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="http://users.isc.tuc.gr/~nmatsatsinis"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Matsatsinis Nikolaos
            Ματσατσινης Νικολαος
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            Doumpos, Michael
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            Grigoroudis, Evangelos
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            Panagiotis Manolitzas
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            Pavlos Delias
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