Course Detail
Units:
3.0
Course Components:
Lecture
Enrollment Information
Enrollment Requirement:
Prerequisite: Graduate standing required and instructor consent.
Description
This course emphasizes health care applications and issues of Knowledge Discovery in Databases (KDD) at an introductory level. The entire KDD process is explored, including creation of target data sets, pre-processing, data mining, pattern interpretation and evaluation, corresponding with the Fayyad model of the KDD process. Lecture and practical exercises survey data mining methods for classification, prediction, rule induction, clustering, and attribute sub-set selection. Later in the course, emphasis shifts to critical analysis of KDD applications in health care.