Development of the Data Preprocessing Agent's Knowledge for Data Mining Using Rough Set Theory
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
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This paper proposes a new agent based approach in rough set classification theory. Rough set is one of data mining techniques for classification. It generates rules from large database and it has mechanism to handle noise and uncertainty in data. However, to produce a rough classification model or rough classifier is highly computational especially in its reduct computation phase which is an np-hard problem. These have contributed to the generation of large amount of rules and lengthy processing time. To resolve the problem, an agent based algorithm is embedded within the rough modelling framework. In this study, the classifier are based on creating agent within the main modelling processes such as reduct computation, rules generation and attribute projections. Four main agents are introduced i.e. interaction agent, weighted agent, reduction agent and default agent. The experimental result shows that the proposed method reduces the running time with a comparative classification accuracy and number of rules.