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YZZ211 - Introduction to Data Mining (Autumn Term)

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Course Information

  • Venue: Class Z3, Department Floor, Annex Building, Faculty of Arts and Sciences

  • Date&Time: 13:15-16:00 on Thursdays

  • Objectives: This course aims to introduce the fundamental concepts, algorithms, and applications of data mining. Students will learn how to explore, preprocess, and analyse data to discover meaningful patterns, and will gain hands-on experience applying core data mining techniques to real-world data sets. This course is designed to build the algorithmic and conceptual foundation required for subsequent courses in Machine Learning and Deep Learning.

  • Textbook:

    • Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and ​Vipin Kumar, Introduction to Data Mining, 2nd Ed., Pearson, 2019.

  • Auxiliary Sources:

    • Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th Ed., Elsevier, 2023.​

    • Michael Hahsler, An R Companion for Introduction to Data Mining, 1st Ed., 2025.

    • Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, and Kenneth C. Lichtendahl, Jr., Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 1st Ed., Wiley, 2018.

    • Galit Shmueli, Peter C. Bruce, Peter Gedeck, and Nitin R. Patel, Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, 1st Ed., Wiley, 2020.​

    • Di Wu,  Data Mining with Python: Theory, Application, and Case Studies, 1st Ed., CRC Press, 2024.​

  • Contents: Introduction to Data Mining, Data (Types of Data, Data Quality, Data Preprocessing), Classification (Decision Tree Induction, Model Evaluation, Overfitting, Confusion Matrix, Accuracy, Precision, Recall, kNN), Association Analysis (Support, Confidence, the Apriori Algorithm, Rule Generation, Case Study: Market Basket Analysis), Cluster Analysis (K-means Algorithm, Hierarchical Clustering, Introduction to DBScan), Anomaly Detection (Statistical and Distance-Based Approaches), Introduction to Ensemble Methods, and Final Project Presentations.

  • Learning Outcomes: Upon successful completion of this course, students will be able to:

    • Identify data quality issues and apply fundamental data preprocessing techniques to prepare raw data for analysis.​

    • Construct decision tree classifiers and the kNN algorithm, and evaluate model performance using metrics such as accuracy, precision, and recall.

    • Apply the Apriori algorithm to generate association rules and interpret their relevance through case studies such as market basket analysis.

    • Implement clustering techniques including K-means and hierarchical clustering, and critically compare their outcomes on real-world data sets.

    • Explain the principles of anomaly detection and apply statistical and distance-based approaches to identify outliers within a data set.

    • Describe the underlying principles of ensemble methods and their role as a foundation for more advanced machine learning techniques.

    • Design, implement, and present an end-to-end data mining project that integrates data preprocessing, modelling, and evaluation on a real-world data set.

  • Assessment and Evaluation:

    • Midterm Exam (40%),​

    • Final Project Presentation (20%),

    • Final Exam (40%).

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Lecture Notes

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Exams

  • Week 8: Midterm Examination (2026)

  • Week 16: Final Examination (2027)

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