Data Mining

AY2017/2018 Semester 1

Basic concepts of data mm1ng, and the data mmmg process. Data preparation including data cleaning, integration, transformation and dimension reduction. Exploratory data analysis. Association rule mining. Predictive modeling using multiple linear regression, logistic regression, decision tree induction, neural network modeling, Bayesian learning, K-nearest neighbor, and support vector machine. Unsupervised learning methods, including cluster analysis and Kohonen self-organizing maps. Applications of data mining.