Knowledge discovery overview, data mining in general, data preparation, basics of data mining, association rule mining, classification and prediction, cluster analysis, web mining, data mining applications, term projects.

Course Objectives:

• Understand the KDD Process: Students will master the full Knowledge Discovery in Databases pipeline, from initial data cleaning and preprocessing to the final interpretation of discovered patterns.
• Implement Core Mining Algorithms: Students will gain hands-on experience in implementing and applying essential algorithms for association rule mining, supervised classification, and unsupervised clustering.
• Evaluate Model Performance: Participants will learn to rigorously assess the quality of data models using industry-standard metrics such as precision, recall, and silhouette coefficients.
• Apply Statistical Foundations: Students will utilize statistical descriptions and distance measures to analyze data similarity and reduce dimensionality through different techniques.

Recommended or Required Reading:

Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan kaufmann.

Learning Outcomes

• Learn the process of knowledge discovery and data mining algorithms
• Gain the ability of using data mining tools
• Understand a data mining problem, design the process overflow and implement the steps
• Document and present a Project

Grading:

• Assignments: 30%
• Project: 10%
• Midterm Exam: 20%
• Final Exam: 40%