MSIA 421: Data Mining

Quarter Offered

Winter ; Edward C. Malthouse


Clustering (k-means, partitioning), association rules, factor analysis, scale development, and survival analysis.

This course will define “data mining” and discuss its relationship with “probabilistic/statistical models.” Both approaches consist of two types of models, supervised learning models, where the objective is to uncover and model structure in the joint density of multiple observed variables. The focus of this course will be on understanding and using unsupervised learning methods, since MSiA 401 (Statistical Methods for Data Mining) and 420 (Predictive Analytics) cover supervised approaches.