CSCE 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: ( CSCE 31903 or CSCE 319H3 or DASC 21003 ) or ( CSCE 20104 and INEG 23303 and INEG 23104 ) or ( CSCE 20104 and STAT 30133 and STAT 30043 )). (Typically offered: Fall)
Prerequisites: CSCE 31903, DASC 21003, CSCE 20104, INEG 23303, INEG 23104, STAT 30133, STAT 30043