Abstract
In order to analyze the distribution of mind-sets (collections of evaluations) in a group, a hierarchical clustering of decision tables has been examined. By the method, we know clusters of mind-set but the clusters are not always optimal in some criterion. In this paper, we develop non-hierarchical clustering techniques for decision tables. In order to treat positive and negative evaluations to a common profile, we use a vector of rough membership values to represent individual opinion to a profile. Using rough membership values, we develop a K-means method as well as fuzzy c-means methods for clustering decision tables. We examined the proposed methods in clustering real world decision tables.
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Inuiguchi, M., Enomoto, R., Kusunoki, Y. (2010). Non-hierarchical Clustering of Decision Tables toward Rough Set-Based Group Decision Aid. In: Torra, V., Narukawa, Y., Daumas, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2010. Lecture Notes in Computer Science(), vol 6408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16292-3_20
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DOI: https://doi.org/10.1007/978-3-642-16292-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16291-6
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