EAGER: Knowledge Transfer Oriented Data Mining with Focus on the Decision Trees Knowledge Type

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This project is to study knowledge transfer oriented data mining (orKTDM). Given two data sets, the idea of KTDM is to discover modelsthat are common to both data sets, as well as models that are uniquein one data set. These common and unique models with respect to thetwo data sets will provide a tool to leverage the already-understoodproperties of one data set for the purpose of understanding the other,probably less understood, data set. This EAGER project is toconcentrate on models in the form of a diversified set ofclassification trees. The KTDM approach is useful for real-worldapplications in part due to its ability to allow users to narrow downto particular models, guided by known knowledge from another data set.It will help towards realizing transfer of knowledge and learning invarious domains. The project will support a graduate student and willseek collaboration with experts in the medical domain. These willincrease the impact of the project. For more information, please seehttp://www.cs.wright.edu/~gdong/projects.html.