Agent-based cooperative heterogeneous data mining
|Bibliography: p. 126-135
|This thesis presents an agent-based cooperative data mining model named CoLe2. CoLe2 is targeted at performing data mining on large, heterogeneous data sets. It employs multiple different types of data mining algorithms, enables cooperations among these algorithms, and produces combined results in the form of rules. CoLe7- is a multi-agent system with three types of agents that have the different roles of running data mining algorithms, performing combination of mining results, and driving the entire CoLe2 system work flow with knowledge-based strategies, respectively. The system has a work flow with two levels of loops. The outer loop performs data selection, mining algorithm selection and expectation adjustment strategies. The inner loop performs data mining execution and result combination, with additional knowledge-based strategies implemented in the agents. The agents exchange useful information during the running of the work flow to help each other. A prototype system of the CoLe2 model is described. This prototype contains four different data mining algorithms (a classification algorithm, a sequence mining algorithm, an association rules mining algorithm and a descriptive mining algorithm), two combination strategies and instantiations of the knowledge-based strategies. The strategies instantiations include data selection based on a clustering algorithm, an asynchronous work flow for better turnaround time, relevance factor calculation, fuzzy condition matching, prediction histogram based rule similarity and rule grouping. Experiments have been performed with two data sets - a medium-sized data set of billing data from Calgary Health Region, and a large data set from the Alberta Kidney Disease Network. The experimental results show advantages of Cole? over individual data mining algorithms in terms of efficiency and result quality, as well as advantages over the CoLe model with only one level of work flow. Specialized experiments also prove the effectiveness of individual knowledge-based strategies.
|ix, 150 leaves : ill. ; 30 cm.
|Gao, J. (2012). Agent-based cooperative heterogeneous data mining (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/4819
|University of Calgary
|University 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.
|Agent-based cooperative heterogeneous data mining
|University of Calgary
|Doctor of Philosophy (PhD)
|Theses Collection 58.002:Box 2086 627942958