闪播福利库

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, na茂ve 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: (颁厂颁贰听31903 or 颁厂颁贰听319贬3 or 顿础厂颁听21003) or (颁厂颁贰听20104 and 滨狈贰骋听23303 and 滨狈贰骋听23104) or (颁厂颁贰听20104 and 厂罢础罢听30133 and 厂罢础罢听30043)). (Typically offered: Fall)