Abstract
Since its inception in early 80’s, the rough set theory has attracted a lot of interest from global research community. It turns out as useful in building classification and prediction models. It complements a number of other soft computing paradigms. It may be combined with fuzzy logic and probabilistic data analysis. It has led towards enhancements of neural networks, genetic algorithms, clustering, support vector machines, regressionmodels, et cetera. Its application domains include pattern recognition, feature selection, information retrieval, bioinformatics, computer vision, multimedia, medicine, retail data mining, web mining, control, traffic engineering, data warehousing, and many others.
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© 2009 Springer-Verlag Berlin Heidelberg
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Joshi, M., Bhaumik, R.N., Lingras, P., Patil, N., Salgaonkar, A., Ślęzak, D. (2009). Rough Set Year in India 2009. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_7
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DOI: https://doi.org/10.1007/978-3-642-10646-0_7
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