Abstract
Although data clustering is relatively uninvestigated in rough set studies, there are much room for applying clustering and related techniques to this field. In this paper we focus on generalization of agglomerative clustering to information systems. A poset-valued hierarchical clustering is defined and the combination of traditional agglomerative clustering and lattice diagram of attributes in an information system is considered. Inner product spaces are available to information systems by using kernel functions in support vector machines. Different algorithms for generalized agglomerative clustering using the inner product are described. Illustrative examples are shown.
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Miyamoto, S. (2008). Generalized Agglomerative Clustering with Application to Information Systems. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2008. Lecture Notes in Computer Science(), vol 5285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88269-5_15
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DOI: https://doi.org/10.1007/978-3-540-88269-5_15
Publisher Name: Springer, Berlin, Heidelberg
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