Data Mining in R

This set of learning materials for undergraduate and graduate data mining class is currently maintained by Xiaorui Zhu. Many materials are from Dr. Yan Yu’s previous class notes. Thanks for the contribution from previous Ph.D. students in Lindner College of Business. Thanks to Dr. Brittany Green for recording the videos.

Lecture and Lab Notes

Introduction to Data Mining and R

Exploratory Data Analysis

Linear Regression, Prediction and Variables Seleciton

Logistic Regression

Cross Validation

Tree Models

Advanced Tree Models: Bagging, Random Forests, and Boosting Tree

Nonlinearity, Generalized Additive Models (GAM), and Nonparametric Smoothing

Neural Network, LDA, and SVM

Unsupervised Learning: Clustering

Unsupervised Learning: Association Rules

Other Topics 1: Basic Text Mining

Basic Text Mining

Contributors:

• Tracy Zhou Wu, Ph.D. (2008). Executive Director/VP, JPMorgan Chase, Dallas, TX.
• Shaonan Tian, Ph.D. (2012). Tenure Track Assistant Prof. at San Jose State University, CA.
• Chaojiang Wu, Ph.D. (2013). Tenure Track Assistant Prof. at Drexel University, PA (now Kent State University, OH).
• Feng Mai, Ph.D., Assistant Professor of Information Systems in the School of Business at Stevens Institute of Technology
• Shaobo Li, Ph.D. (2018). Tenure Track Assistant Prof. at University of Kansas.
• Yuankun Zhang, Ph.D. (2018). VP, Bank of New York Mellon, Pittsburgh, PA.
• Brittany Green, Ph.D. (2020). Tenure Track Assistant Prof. at University of Louisville.
• Xiaorui Zhu, Tianhai Zu, Saidat Sanni, Zewei Lin, ongoing Ph.D. students.
• Zhiyuan Dong, Ph.D. Principal, Media Center of Excellence at IRI, Chicago
• Wei Xiong, Jingyin Gene, ChongQing, China