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

Wright State University, Dayton OH

Investigators

Abstract

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