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

本课题研究面向知识转移的数据挖掘(KTDM)。在给定两个数据集的情况下，KTDM的想法是发现两个数据集共有的模型，以及一个数据集中唯一的模型。关于这两个数据集的这些共同和独特的模型将提供一种工具，以利用一个数据集已经被理解的属性，以理解另一个可能不太被理解的数据集。这个迫切的项目是以一组多样化的分类树的形式专注于模型。KTDM方法对实际应用很有用，部分原因是它允许用户在来自另一个数据集的已知知识的指导下，将范围缩小到特定的模型，这将有助于实现知识的转移和学习不变的领域。该项目将支持一名研究生，并将寻求与医学领域的专家合作。这些都将增加该项目的影响。欲了解更多信息，请登录：seehttp://www.cs.wright.edu/~gdong/projects.html.。

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.