CMPUT 697 - Intro to Knowledge Discovery in Databases & Data Mining
Overview
This course will introduce principles of data mining with a focus on unsupervised methods for high-dimensional and spatial data. In the first part of the course, after an overview of Knowledge Discovery and Data Mining, we will study principles and selected data mining methods for clustering, classification, outlier detection, finding association rules, and spatial data mining, which will include index structures and basic query processing in spatial and multi-dimensional databases. The course will be strongly research oriented in both the lectures and the course projects.
Objectives
- Understanding of data mining methods (clustering, outlier detection, classification, association rules, selected methods for spatial data mining, ...)
- Understanding of data management issues for spatial and multi-dimensional data
- Understanding of limitations of the data mining methods for high-dimensional data
- Be able to critically read, review, and present research papers in the subject area
- Be able to apply selected data mining methods
- Be able to do formulate and conduct a research project in the area of data mining
Course Work
- Assignments
- Projects
- Presentations
- Midterms
Related Research Areas
- Database Systems
- Machine Learning