CSCI 550 - Data Mining

The goal of this course is to present in detail a number of fundamental data mining techniques , and to introduce a few, new and interesting aspects of data mining-related research. In the first part of the course the following components of data mining will be introduced: (1) Data Preprocessing, (2) Frequent Itemsets Discovery, (3) Clustering, and (4) Classification. If time allows, we may also go through a couple of industrial DM methodologies (e.g. CRISP-DM, SEMMA), or cover some of the "hot" data mining topics (e.g. Graph-data Mining, Text Mining, Spatial Data Mining). These lectures should provide graduate students with sufficient foundation to conduct their own, but supervised research in the field of data mining. Students will gain hands on experience on the chosen aspect of data mining area through completion of an individual graduate-level research project, which will be reviewed by their peers, and graded by the instructor at the end of the semester

The web page of this course can be found here and sample of Syllabus is here.

Credits: 3 Lec
Offered: Fall Semester in Even Years
Audience: Graduate Students