C4.5: programs for machine learning
C4.5: programs for machine learning
Fast discovery of association rules
Advances in knowledge discovery and data mining
Toward Multidatabase Mining: Identifying Relevant Databases
IEEE Transactions on Knowledge and Data Engineering
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Planning for Distributed Theorem Proving: The Teamwork Approach
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Using Distributed Data Mining and Distributed Artificial Intelligence for Knowledge Integration
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A cooperative multi-agent data mining model and its application to medical data on diabetes
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
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We present an extension of the usual agent-based data mining cooperative work flow that adds a so-called adjustment work flow. It allows for the use of various knowledge-based strategies that use information gathered from the miners and other agents to adjust the whole system to the particular data set that is mined. Among these strategies, in addition to the basic exchange of hints between the miners, are parameter adjustment of the miners and the use of a clustering miner to select good working data sets. Our experimental evaluation in mining rules for two medical data sets shows that adding a loop with the adjustment work flow substantially improves the efficiency of the system with all the strategies contributing to this improvement.