Abstract
Clustering which is one of the pattern recognition methods is a technique automatically classifying data into some clusters. Various types of clustering are divided broadly into hierarchical and non-hierarchical clustering and crisp and fuzzy set theories have been applied to non-hierarchical clustering. Recently, clustering based on rough set theory has been attracted. Rough clustering represents a cluster by using two layers, i.e., upper and lower approximations. This paper proposes a c-regression method based on rough set representation which does regression analysis and clustering at the same time. Moreover, its effectiveness is shown through numerical examples.
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Endo, Y., Sugawara, A., Kinoshita, N. (2013). Rough c-Regression Based on Optimization of Objective Function. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_24
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DOI: https://doi.org/10.1007/978-3-642-41550-0_24
Publisher Name: Springer, Berlin, Heidelberg
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