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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578891
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dc.contributor.authorNurul Adzlyana M. S (UITM)
dc.contributor.authorRosma M. D (UITM)
dc.contributor.authorNurazzah A. R (UITM)
dc.date.accessioned2023-11-06T03:09:49Z-
dc.date.available2023-11-06T03:09:49Z-
dc.date.issued2017-04
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116307
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578891-
dc.descriptionData mining processes such as clustering, classification, regression and outlier detection are developed based on similarity between two objects. Data mining processes of categorical data is found to be most challenging. Earlier similarity measures are context-free. In recent years, researchers have come up with context-sensitive similarity measure based on the relationships of objects. This paper provides an in-depth review of context-based similarity measures. Descriptions of algorithm for four context-based similarity measure, namely Association-based similarity measure, DILCA, CBDL and the hybrid context-based similarity measure, are described. Advantages and limitations of each context-based similarity measure are identified and explained. Context-based similarity measure is highly recommended for data-mining tasks for categorical data. The findings of this paper will help data miners in choosing appropriate similarity measures to achieve more accurate classification or clustering results.
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-25-2-4
dc.rightsUKM
dc.subjectCategorical data
dc.subjectContext-based
dc.subjectData mining
dc.subjectSimilarity measure
dc.titleReview of Context-Based Similarity Measure for Categorical Data
dc.typeJournal Article
dc.format.volume25
dc.format.pages619-630
dc.format.issue2
Appears in Collections:Journal Content Pages/ Kandungan Halaman Jurnal

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