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Knowledge Discovery in Databases (KDD) is an active research area with the promise for a high payoff in many business and scientific applications. The grand challenge of knowledge discovery in databases is to automatically process large quantities of raw data, identify the most significant and meaningful patterns, and present this knowledge in an appropriate form for achieving the user's goal. Knowledge discovery systems face challenging problems from the real-world databases which tend to be very large, redundant, noisy and dynamic. Each of these problems has been addressed to some extent within machine learning, but few, if any, systems address them all. Collectively handling these problems while producing useful knowledge efficiently and effectively is the main focus of the thesis. In this thesis, we develop an attribute-oriented rough set approach for knowledge discovery in databases. The method adopts the artificial intelligent "learning from examples" paradigm combined with rough...

Database stores a huge amount of information in a structured and organized manner and provides many features for machine learning. There are a lot of algorithms to discover different kinds of rules from databases. In this paper, we propose a new method which can compute all maximal generalized rules in relational databases. The method integrates the machine learning paradigm, especially learning-from-examples techniques, with rough-set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the decision matrices are constructed from the generalized relation and the maximal generalized rules with non-necessary constraints can be learned. The authors are members of the Institute for Robotics and Intelligent Systems (IRIS) and wish to acknowledge the support of the Networks of Centres of Excellence Program of the Government of Canada, the Natural ...

. Today's Data Base Management Systems do not provide functionality to extract potencially hidden knowledge in data. This problem gave rise in the 80's to a new research area called Knowledge Discovery in Data Bases (KDD). In spite the great amount of research that has been done in the past 10 years, there is no uniform mathematical model to describe various techniques of KDD. The main goal of this paper is to describe such a model. The Model integrates in an uniform framework various Rough Sets Techniques with standard, non Rough Sets based techniques of KDD. The Model has been already partially implemented in RSDM (Rough Set Data Miner) and we plan to complete the implementation by integrating all the operations in the code of database management systems. Operations that are defined in the paper have successfully been implemented as part of RSDM. 1 Introduction KDD process was first defined as the 'non trivial process of extracting valid potentially useful and ultimate...

Abstract—Rough set is a tool in analyzing and processing the imprecise, inconsistent and incomplete information, and finding the connotative knowledge, potential regulations and methods. Aiming at the imprecise and uncertainty of factors in business decision support system, the paper brings out an algorithm based on rough set for rules gaining to analyze and process data, minimal decision-making rules are proposed. Finally, an example in decision support system is introduced to confirm the algorithm’s validity. Index Terms — Rough set, reduction, rules gaining. I.