On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
Trends in Databases: Reasoning and Mining
IEEE Transactions on Knowledge and Data Engineering
Distributed mining of classification rules
Knowledge and Information Systems
The Inconsistency in Rough Set Based Rule Generation
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Supervised Learning: A Generalized Rough Set Approach
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
GRS: a generalized rough sets model
Data mining, rough sets and granular computing
Attribute reduction based on granular computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Review: Soft computing applications in customer segmentation: State-of-art review and critique
Expert Systems with Applications: An International Journal
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In this paper, the principle and experimental results of an attribute-oriented rough set approach for knowledge discovery in databases are described. Our method integrates the database operation, rough set theory and machine learning techniques. In our method, we consider the learning procedure consists of two phases: data generalization and data reduction. In data generalization phase, the attribute-oriented induction is performed attribute by attribute using attribute removal and concept ascension, some undesirable attributes to the discovery task are removed and the primitive data is generalized to the desirable level, thus a set of tuples may be generalized to the same generalized tuple, this procedure substantially reduces the computational complexity of the database learning process. Subsequently, in data reduction phase, the rough set method is applied to the generalized relation to find a minimal attribute set relevant to the learning task. The generalized relation is reduced further by removing those attributes which are irrelevant and/or unimportant to the learning task. Finally the tuples in the reduced relation are transformed into different knowledge rules based on different knowledge discovery algorithms. Based upon these principles, a prototype knowledge discovery system DBROUGH has been constructed. In DBROUGH, a variety of knowledge discovery algorithms are incorporated and different kinds of knowledge rules, such as characteristic rules, classification rules, decisions rules, maximal generalized rules can be discovered efficiently and effectively from large databases.