Classification via k-means clustering and distance-based outlier detection

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dc.contributor.authorSurasit Songma
dc.contributor.authorWitcha Chimphlee
dc.contributor.authorKiattisak Maichalernnukul
dc.contributor.authorParinya Sanguansat
dc.date.accessioned2025-03-10T07:37:40Z
dc.date.available2025-03-10T07:37:40Z
dc.date.issued2012
dc.description.abstractWe propose a two-phase classification method. Specifically, in the first phase, a set of patterns (data) are clustered by the k-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The Knowledge Discovery Databases (KDD) Cup 1999 data set, which has been utilized extensively for development of intrusion detection systems, is used in our experiment. The results show that the proposed method is effective in intrusion detection. © 2012 IEEE.
dc.identifier.citationInternational Conference on ICT and Knowledge Engineering
dc.identifier.doi10.1109/ICTKE.2012.6408540
dc.identifier.isbn978-146732314-7
dc.identifier.issn2157099X
dc.identifier.scopus2-s2.0-84873376817
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4975
dc.languageEnglish
dc.rights.holderScopus
dc.subjectClassification
dc.subjectintrusion detection
dc.subjectk-means
dc.subjectKDD Cup 1999 data set
dc.subjectoutlier detection
dc.titleClassification via k-means clustering and distance-based outlier detection
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84873376817&doi=10.1109%2fICTKE.2012.6408540&partnerID=40&md5=8f7568a2db7f1abc937f49d7335548e5
oaire.citation.endPage128
oaire.citation.startPage125
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