INEG 41403 — Data Mining
The course focuses on the principles, theory, design, and implementation of data mining algorithms for large-scale data. Topics include foundations of data mining; preprocessing; mining frequent patterns, associations and correlations; supervised learning including decision tree induction, naïve Bayesian classification, support vector machine, logistic regression, Bayesian network, and K-nearest neighbor learning; unsupervised learning including K-means clustering, hierarchical clustering, density-based clustering, and grid-based clustering; outlier analysis; graph mining; scalable and distributed data mining. Prerequisite: ( INEG 23303 and INEG 22203 ) or ( CSCE 20104 and INEG 33103 ) or ( INEG 23104 and INEG 22203 ) or INEG 33303 . (Typically offered: Fall)
Prerequisites: INEG 23303, INEG 22203, CSCE 20104, INEG 33103, INEG 23104, INEG 33303