ROUGHAGENT TM proposes a new agent based approach in rough set classification theory with a default rule generation framework. ROUGHAGENT is a data mining tool that have the combination two basic theories that are :
1. the theory of agent in rough set
2. the theory of agent in the default rules generation framework.
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. This drawback has contributed to the generation of large amount of rules and lengthy processing time. To resolve the problem, an agent is embedded within a rough classifier framework specifically the Rough Set Default Rules Generation Framework (DRGF). In this research project, an agent is created within the main rough set modeling processes such as reduct computation, rules generation and attribute projections. Four main agents are introduced ROUGHAGENT i.e interaction agent, weighted agent, reduction agent and default agent.