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CoLe: A Cooperative Distributed Data Mining Model

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Author
Gao, Jie
Denzinger, Jorg
James, Robert C.
Accessioned
2008-02-27T16:58:52Z
Available
2008-02-27T16:58:52Z
Computerscience
2005-03-08
Issued
2005-03-08
Subject
Computer Science
Type
unknown
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Abstract
We present CoLe, a cooperative, distributed 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 that 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 CoLe-based system 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 the ability to find useful rules. From the medical perspective, our system confirmed hypertension has a tight relation to diabetes, and it also suggested connections new to medical doctors.
Notes
We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.ca
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University of Calgary
Faculty
Science
Doi
http://dx.doi.org/10.11575/PRISM/30577
Uri
http://hdl.handle.net/1880/45849
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