Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Information agents cooperating with heterogenous data sources for customer-order management
Proceedings of the 2004 ACM symposium on Applied computing
Agent-Based Pattern Mining of Discredited Activities in Public Services
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
An Agent Based Rough Classifier for Data Mining
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
A method for handling numerical attributes in GA-based inductive concept learners
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An architecture for distributed agent-based data preprocessing
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Execution engine of meta-learning system for KDD in multi-agent environment
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
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Data preprocessing is one of the important task in Knowledge Discovery in Databases or Data Mining. The preprocessing is complex and tedious task especially involving large dataset. It is crucial for a data miner to be able to determine the appropriate data preprocessing techniques for a particular data set as it will save the processing time and retain the quality of the data for data mining. Current data mining researchers use agent as a tool to assist data mining process. However, very few researches focus on using agent in the data preprocessing. Applying agents with autonomous, flexible and intelligence reduced the cost of having a quality, precise and updated data or knowledge. The most important part of having an agent to perform data mining task particularly data preprocessing is the generation of agent's knowledge. The data preprocessing agent's knowledge are meant for agent to decide the appropriate data preprocessing technique to be used on a particular dataset. Therefore, in this paper we propose a methodology for creating the data preprocessing agent's knowledge by using rough set theory. The experimental results showed that the agent's knowledge generated is significant to be used for automated data preprocessing techniques selection.