A new similarity measure in formal concept analysis for case-based reasoning

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dc.contributor.authorTadrat, Jirapond
dc.contributor.authorBoonjing, Veera
dc.contributor.authorPattaraintakorn, Puntip
dc.date.accessioned2026-08-06T10:03:54Z
dc.date.available2026-08-06T10:03:54Z
dc.date.issued2012-01-01
dc.description.abstractIn this work, we aim at developing a better knowledge base by using formal concept analysis (FCA) and propose its new similarity measure based on vector model for case-based reasoning (CBR). The features of our proposed approaches are illustrated using a part of CBR system for both classification and problem-solving. Concept lattice knowledge base provides more accuracy classification for hierarchical data structure when comparing with non-hierarchical data structure. Dependency induced from our concept lattice knowledge base can help to suggest informative solutions for problem-solving CBR. In addition, our similarity measure improves the accuracy of classification CBR significantly when we perform experiments on the UCI data sets with cross validation. © 2011 Elsevier Ltd. All rights reserved.
dc.identifier.citationExpert Systems with Applications, 39(1), 967-972, 2012
dc.identifier.doi10.1016/j.eswa.2011.07.096
dc.identifier.issn09574174
dc.identifier.other2-s2.0-84860402443
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4100
dc.sourceExpert Systems with Applications
dc.subjectCase-based reasoning
dc.subjectConcept similarity
dc.subjectFormal concept analysis
dc.subjectKnowledge representation
dc.titleA new similarity measure in formal concept analysis for case-based reasoning
dc.typeArticle

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