Fall 2026 Massive Data Mining

Lectures: Wednesday 8:30am-11:30am.
Instructor: Ruixiang Tang
Office Hour: Wednesday 1pm-2pm

Course Overview

This course introduces the fundamental concepts, techniques, and applications of data mining, with an emphasis on analyzing and extracting knowledge from massive and complex datasets. Students will learn core methods including classification, association analysis, clustering, and anomaly detection, as well as strategies for evaluating and validating models to ensure reliability. The course will also address challenges unique to large-scale data, such as scalability, distributed frameworks, and avoiding false discoveries. Through lectures, readings, and hands-on exercises, students will gain both theoretical understanding and practical experience in applying data mining methods to real-world problems in science, engineering, business, and healthcare.

Grading

5 Quizzes

3 Research Papers (also research paper-based quizzes)

Midterm Exam

Final Semester Project

Course Schedule (tentative)

Week#	Title	Topics
Week 1	Introduction to Data Mining	Course Overview and Introduction Environment Setup and Tools Introduction to Python for Data Science
Week 2	Data and Data Preparation	Types of data, data quality, preprocessing (normalization, sampling, dimensionality reduction).
Week 3	Classification: Decision Trees	Tree construction, splitting criteria (Gini, information gain), pruning.
Week 4	Model Evaluation & Overfitting	Training vs. testing, cross-validation, bias–variance tradeoff, ROC/AUC.
Week 5	Rule-Based Classifiers & k-NN	IF-THEN rules, covering vs. decision tree rules, nearest neighbor methods.
Week 6	Naïve Bayes & Neural Networks	Bayes theorem, independence assumption, perceptrons, feed-forward NNs.
Week 7	SVM, Ensemble Methods, Class Imbalance	Large-margin classifiers, bagging, boosting, random forests, rebalancing techniques.
Week 8	Association Analysis: Basics	Market basket analysis, frequent itemset mining, Apriori, support & confidence.
Week 9	Association Analysis: Advanced	Sequential patterns, graph patterns, interestingness measures.
Week 10	Clustering: Partitional Methods	K-means, initialization, distance measures, evaluation metrics.
Week 11	Clustering: Advanced Methods	Hierarchical clustering, DBSCAN, spectral clustering, cluster validation.
Week 12	Anomaly Detection	Statistical, distance-based, density-based, clustering-based approaches.
Week 13	Avoiding False Discoveries	Hypothesis testing, multiple comparisons, p-values, reproducibility.
Week 14	Scalability & Big Data Mining	Scalability issues, distributed data mining, streaming data, wrap-up.