CS 6665: Data Mining

Spring 2023, 3:00 pm to 4:15 pm on TR, Old Main 117

The information described here has not been finalized yet. This page will be updated frequently.

Course Descriptions

Data mining aims at finding useful patterns in large data sets. This course will discuss data mining algorithms for analyzing large amounts of data, including association rules mining, finding similar items, clustering, data stream mining, recommender systems, how search engines rank pages, and recent techniques for large scale machine learning. The goal of this class is for students to understand basic and scale data mining algorithms.

Prerequisites

  • A solid programming skill (Python is preferred)
  • Basic probability and statistics
  • Basic linear algebra

Course Material

  • [MMDS] Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets. Cambridge university press. Available Online.
  • [MML] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. (2020). Mathematics for Machine Learning. Available Online.

Class Schedule

DateTopicReading
Jan 10Introduction to Data MiningMMDS CH.1
Jan 12Map-ReduceMMDS CH.2
Jan 17Matrix Multiplication by MapReduce (Optional)MMDS CH.2
Jan 19SparkMMDS CH.2
Jan 24Frequent Itemset MiningMMDS CH.6.1-6.3; CH.6.4 (Optional)
Jan 26Locality-Sensitive HashingMMDS CH.3.1-3.4
Jan 31Locality-Sensitive HashingMMDS CH.3.5-3.6 (Optional)
Feb 2ClusteringMMDS CH.7.1-7.3
Feb 7Hierarchical clustering and K-meansMMDS CH.7.1-7.3
Feb 9BRF and CUREMMDS CH.7.3-7.4
Feb 14EM algorithm (Optional)MML CH.11.1-11.3
Feb 16Gaussian Mixture Models (Optional)MML CH.11.1-11.3
Feb 21k-nn and Naive BayesMMDS CH.12.1,12.4
Feb 23k-nn and Naive BayesMMDS CH.12.1,12.4
Feb 28SVMMML CH.12.1,12.2 MMDS CH.12.3
Mar 2Course Project Proposal Presentation 
Mar 7Spring Break 
Mar 9Spring Break 
Mar 14SVMMML CH.12.1,12.2 MMDS CH.12.3
Mar 16Decision TreeMMDS CH.12.5
Mar 21PageRankMMDS CH5.1-5.2
Mar 23Dimensionality ReductionMMDS CH.11.3
Mar 28Recommender SystemsMMDS CH.9.1-9.3
Mar 30Mining Data StreamsMMDS CH4.1-4.3
April 4Mining Data StreamsMMDS CH4.4-4.7
April 6Mining Data StreamsMMDS CH4.4-4.7
April 11Trustworthy AI 
April 13Course Project Presentation 
April 18Course Project Presentation 
April 20Course Project Presentation 
April 25Canceled 
April 27Final Exam3:00 PM – 4:30 PM

Grading

  • Homework (35%)
    • Six programming assignments
  • Course Project (30%)
    • Students are required to participate in one Kaggle competition.
    • The project will be evaluated based on the technical soundness, presentation, and final report.
  • Final Exam (35%)

  • Class Attendance
    • Class attendance is not mandatory but recommended.

Course Topics

  1. Data mining overview
  2. MapReduce and Spark
  3. Frequent itemset mining
  4. Finding similar items
  5. Clustering
  6. Classification
  7. Mining data stream
  8. Dimensionality reduction
  9. Recommender systems
  10. Computational advertising
  11. Pagerank
  12. Anomaly detection