DBROUGH: A rough set based knowledge discovery system
Knowledge discovery in databases, or data mining, is an important objective in the development of data- and knowledge-base systems. An attribute-oriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of the database learning processes. Then the rough set techniques are applied to the generalized relation to derive different knowledge rules. Moreover, the approach can find all the maximal generalized rules in the data. Based on these principles, a prototype database learning system, DBROUGH, has been constructed. Our study shows that attribute-oriented induction combined with rough set techniques provide an efficient and effective mechanism for knowledge discovery in database systems.
Keywords kwMachine LearningKnowledge Discovery in Databases Methodologies
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