A cooperative distributed data mining model and its application to medical data on diabetes
We present CoLe, a cooperative distributed system model for mining knowledge from heterogeneous data. CoLe allows for the cooperation of different learning algorithms and the combination of the mined knowledge into knowledge structures no individual learner can produce. CoLe organizes the work in rounds so that knowledge discovered by one learner can help others in the next round. We implemented a system based on CoLe for mining diabetes data, including a genetic algorithm for learning event sequences, improvements to the PART algorithm for our problem and combination methods to produce hybrid rules containing conjunctive and sequence conditions. In our experiments, the CoLe-based system outperformed the individual learners, with better rules and more rules of a certain quality. Our improvements to learners also showed they were useful. From the medical perspective, our system confirmed hypertension has a tight relation to diabetes, and it also suggested connections new to medical doctors.
Bibliography: p. 80-90
Gao, J. (2004). A cooperative distributed data mining model and its application to medical data on diabetes (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/21059