Abstract
Although the approaches are fundamentally different, the derivation of decision rules from information systems in the form of tables can be compared to supervised classification in pattern recognition; in the latter case classification rules should be derived from the classes of given points in a feature space. We also notice that methods of unsupervised classification (in other words, data clustering) in pattern recognition are closely related to supervised classification techniques. This observation leads us to the discussion of clustering for information systems by investigating relations between the two methods in the pattern classification. We thus discuss a number of methods of data clustering of information tables without decision attributes on the basis of rough set approach in this paper. Current clustering algorithms using rough sets as well as new algorithms motivated from pattern classification techniques are considered. Agglomerative clustering are generalized into a method of poset-valued clustering for discussing structures of information systems using new notations in relational databases. On the other hand K-means algorithms are developed using the kernel function approach. Illustrative examples are given.
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Miyamoto, S. (2007). Data Clustering Algorithms for Information Systems. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_2
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DOI: https://doi.org/10.1007/978-3-540-72530-5_2
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