IS 675: Introduction to Data Mining (3 credits)
This course is designed to provide an introduction to data mining concepts and techniques. The course will include both theoretical foundations of commonly used data mining methods as well as hands-on exercises using data mining tools. Topics will include techniques such as association rules, classification, and clustering. Various algorithms on each of these techniques will be covered in the course. Examples of such algorithms include the apriori algorithm for association rules; Bayesian classifiers, networks, and decision trees for classification; and k-means, its variants, and hierarchical algorithms for clustering. Several real-life applications will be discussed for each of these techniques. The course will include regular class discussions based on the materials from the textbook, quizzes and assignments, and one examination.
Students must successfully complete IS 633 or an equivalent prior to enrolling in this course.
Elementary knowledge of statistics and programming are recommended for this course.