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A Cooperative Multi-agent Data Mining Model and Its Application to Medical Data on Diabetes

  • Jie Gao
  • Jörg Denzinger
  • Robert C. James
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3505)

Abstract

We present CoLe, a model for cooperative agents for mining knowledge from heterogeneous data. CoLe allows for the cooperation of different mining agents and the combination of the mined knowledge into knowledge structures that no individual mining agent can produce alone. CoLe organizes the work in rounds so that knowledge discovered by one mining agent can help others in the next round. We implemented a multi-agent system based on CoLe for mining diabetes data, including an agent using a genetic algorithm for mining event sequences, an agent with improvements to the PART algorithm for our problem and a combination agent with methods to produce hybrid rules containing conjunctive and sequence conditions. In our experiments, the CoLe-based system outperformed the individual mining algorithms, with better rules and more rules of a certain quality. From the medical perspective, our system confirmed hypertension has a tight relation to diabetes, and it also suggested connections new to medical doctors.

Keywords

CoLe Model Sequence Rule Mining Agent Conjunctive Rule Single Miner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jie Gao
    • 1
  • Jörg Denzinger
    • 1
  • Robert C. James
    • 2
  1. 1.Department of Computer ScienceUniversity of CalgaryCanada
  2. 2.Aechidna Health InformaticsWinnipegCanada

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