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A new similarity measure in formal concept analysis for case-based reasoning
Author(s)
Tadrat, Jirapond
Boonjing, Veera
Pattaraintakorn, Puntip
Date Issued
January 1, 2012
Type
Article
Abstract
In 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.
Citation
Expert Systems with Applications, 39(1), 967-972, 2012