Denzinger, JörgGao, Jie2005-08-192005-08-1920040612976440http://hdl.handle.net/1880/42429Bibliography: p. 80-90We 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.ix, 93 leaves : ill. ; 30 cm.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.master thesis10.11575/PRISM/21059AC1 .T484 2004 G36