Abstract
In this paper, we propose a rough-granular computing framework for mining relational data. We adapt the tolerance rough set model for relational data analysis. We introduce two ways for constructing the universe from relational data. Due to applying granular computing methods, one can overcome problems such as relational data representation and the search space limitation. We also show how the proposed framework can be applied to data mining tasks such as classification.
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Hońko, P. (2012). Rough-Granular Computing Based Relational Data Mining. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_30
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DOI: https://doi.org/10.1007/978-3-642-31709-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31708-8
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