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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578533
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dc.contributor.authorKhaled Ali Othman (UPM)
dc.contributor.authorMd Nasir Sulaiman (UPM)
dc.contributor.authorNorwati Mustapha (UPM)
dc.contributor.authorNurfadhlina Mohd Sharef (UPM)
dc.date.accessioned2023-11-06T03:03:14Z-
dc.date.available2023-11-06T03:03:14Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116001
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578533-
dc.descriptionK-Means is an unsupervised method partitions the input space into clusters. K-Means algorithm has a weakness of detecting outliers, which have it available in many variations research fields. A decade ago, Rough Sets Theory (RST) has been used to solve the problem of clustering partition. Specifically, Rough K-Means (RKM) is a one of the powerful hybrid algorithm, which has it, has various extension versions. However, with respect of the ideas of existing rough clustering algorithms, a suitable method to detect outliers is much needed now. In this paper, we propose an effective method to detect local outliers in rough clustering. The Local Outlier Factor (LOF) method in rough clustering improves the quality of the cluster partition. The improved algorithm increased the level of clusters quality. An existing algorithm version, the ? Rough K-Means (? RKM) tested in the study. Finally, the effectiveness of the algorithm performance is demonstrated based on synthetic and real datasets.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-S-6
dc.rightsUKM
dc.subjectData analysis
dc.subjectLocal outlier factor
dc.subjectK-Means; Rough clustering
dc.subjectOutlier detection
dc.titleLocal outlier factor in rough k-means clustering
dc.typeJournal Article
dc.format.volume25
dc.format.pages211-222
dc.format.issueSpecial Issue
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