BUA 6210G - Data Mining

Credits: 4

This course introduces students to the fundamental conceptual, operational and experiential aspects of automated data mining approaches and techniques, focusing primarily on the use of pattern recognition algorithms to address common business problems involving classification and prediction. In this course students develop basic theoretical and experiential knowledge of supervised and unsupervised data mining approaches, framed in the context of the common data mining related challenges including dimensionality, regularization, overfitting and generalization. Various pattern and association rules and pattern discovery modes are also addressed, including compressed, sequential and spatial patterns, as well as multi-level and multi-dimensional associations. Lastly, common data mining applications, including clustering and prediction, are discussed and illustrated.