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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References
Enomoto, Y., Harada, T., Inoue, T., Mori, N.: Analysis of Choice for Audio Products Using Annexation Reduct System. The Bull. of Jpn. Soc. for the Sci. of Des. 49(5), 11–20 (2003) (in Japanese)
Inuiguchi, M.: A Multi-Agent Rough Set Model toward Group Decision Analysis. Kansei Eng. Int. 6(3), 33–40 (2006)
Inuiguchi, M.: Three Approaches to Rule Induction from Multiple Decision Tables. In: Proc. of the 12th Czech-Jpn Semin. on Data Anal. and Dec. Mak. under Uncertain., pp. 41–50 (2009)
Inuiguchi, M., Furudono, T.: Clustering Analysis of Individual Opinions Given by Decision Tables. In: Proc. of the 10th Czech-Jpn Semin. on Data Anal. and Dec. Mak. under Uncertain., pp. 41–53 (2007)
Inuiguchi, M., Miyajima, T.: Variable Precision Rough Set Approach to Multiple Decision Tables. In: Ślȩzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 304–313. Springer, Heidelberg (2005)
Itou, K., Enomoto, Y., Harada, T.: Influence of Annexation Order to Plural Annexation Condition Parts of Decision Rules. In: Proc. of 19th Fuzzy Syst. Symp., pp. 529–532 (2003) (in Japanese)
Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications. Springer, Heidelberg (2008)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)
Yamamoto, S., Inuiguchi, M.: A Variable Precision Dominance-based Rough Set Approach to Multiple Decision Tables. In: Inuiguchi, M., et al. (eds.) Proc. of MDAI 2009, pp. 153–164 (2009), CD-ROM
Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46(1), 39–59 (1993)
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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
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