Abstract
Mining information from large databases has been recognized as a key research topic in database systems. The explosive growth of databases has made neccesary to discover techniques and tools to transform the huge amount of stored data, into useful information. Rough Set Theory [17] has been applied since its very beginning to different application areas. This chapter presents an integration of Relational DataBase Management technology with Rough Sets Theory to show how the algorithms can be successfully translated into SQL and used as a powerful tool for knowledge discovery.
As a consecuence, a system has been designed and implemented in our research, called RSDM (Rough Set Data Miner), its architecture as well as its main properties will be further described in this chapter.
This work is supported by the Spanish Ministry of Education under project PB950301
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Fernandez-Baizán, M.C., Menasalvas Ruiz, E., Peña Sánchez, J.M. (2000). Integrating RDMS and Data Mining Capabilities Using Rough Sets. In: Pons, O., Vila, M.A., Kacprzyk, J. (eds) Knowledge Management in Fuzzy Databases. Studies in Fuzziness and Soft Computing, vol 39. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1865-9_22
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DOI: https://doi.org/10.1007/978-3-7908-1865-9_22
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