General Information

• • Class Time: ­Thu 15:00~17:45

  • Location: 26421 (Engineering Building 2)

  • Language: English (except Q&A)

Instructor

• • Prof. Seokho Kang

  • Office: 27408B (Engineering Building 2)

  • E-mail: s.kang@skku.edu

  • Office Hours: by appointment via e-mail

Teaching Assistant

• • Ms. Jisu Yoo

  • Office: 27407 - Data Mining Lab. (Engineering Building 2)

  • E-mail: yoo171084@gmail.com

References

• • Pang-Ning Tan, Michael Steinbach, Anuj Karpatne & Vipin Kumar, Introduction to Data Mining (2nd ed.), Pearson, 2019. (https://www-users.cs.umn.edu/~kumar001/dmbook)

  • Jiawei Han, Micheline Kamber & Jian Pei, Data Mining: Concepts and Techniques (3rd ed.), Morgan Kaufmann Publishers, 2011. (http://hanj.cs.illinois.edu/bk3)

Evaluation

• • Attendance (10%)

  • Assignments (10%)

  • Presentation (10%)

  • Mid-term Exam (30%)

  • Final Exam (40%)

  • Total (100% + a)

Schedule (Subject to Change)

• • Syllabus [download]

Lecture Notes + Further Readings (will be updated soon)

* Most of the lecture slides are adapted from Pang-Ning Tan’s materials, which are available at https://www-users.cs.umn.edu/~kumar001/dmbook
** Futher readings are not mandatory but strongly recommended.

• • [9/1] Course Introduction [download]

    • 1. Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert Systems with Applications, 39(12), 11303-11311.

      2. Wu, X., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37.

  • [9/8] 1. Data [download]

    • 1. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.

      2. Lin, W. C., & Tsai, C. F. (2019). Missing value imputation: a review and analysis of the literature (2006–2017). Artificial Intelligence Review, 1-23.

  • [9/15] 2. Classification: Basic Concepts [download]

    • 1. Kuhn, M., & Johnson, K. (2013). Over-fitting and model tuning. In Applied Predictive Modeling (pp. 61-92). Springer, New York, NY.

      2. Domingos, P. (2000). A unified bias-variance decomposition and its applications. In Proceedings of International Conference on Machine Learning (pp. 231-238).

      3. Cawley, G. C., & Talbot, N. L. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11(Jul), 2079-2107.

  • [9/22] 3. Classification: Algorithms - Part 1 [download]

    • 1. Bottou, L., & Vapnik, V. (1992). Local learning algorithms. Neural computation, 4(6), 888-900.

      2. Garcia, S., Derrac, J., Cano, J., & Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 417-435.

  • [9/29] 4. Classification: Algorithms - Part 2 [download]

    • 1. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121-167.

      2. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

      3. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.

  • [10/6] 5. Classification: Algorithms - Part 3 [download]

    • 1. Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39.

      2. Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221-232.

      3. Kang, S. (2020). Model validation failure in class imbalance problems. Expert Systems with Applications, 113190.

      4. Santos, M. S., Soares, J. P., Abreu, P. H., Araujo, H., & Santos, J. (2018). Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches. IEEE Computational Intelligence Magazine, 13(4), 59-76.

  • [10/13] Review Session (Lectures 1-5, in Korean, optional)

  • [10/20] Mid-Term Exam

  • [10/27] 6. Cluster Analysis - Part 1 [download]

    • 1. Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651-666.

      2. Dhillon, I. S., Guan, Y., & Kulis, B. (2004). Kernel k-means: spectral clustering and normalized cuts. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data mining (pp. 551-556).

  • [11/3] 7. Cluster Analysis - Part 2 [download]

    • 1. Kriegel, H. P., Kröger, P., Sander, J., & Zimek, A. (2011). Density‐based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 231-240.

      2. Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395-416.

      3. Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artificial Intelligence, 25(3), 337-372.

  • [11/10] 8. Anomaly Detection [download]

    • 1. Tax, D. M. (2001). One-class classification. PhD thesis, Delft University of Technology.

      2. Tax, D. M., & Duin, R. P. (2004). Support vector data description. Machine Learning, 54(1), 45-66.

      3. Ruff, L. et al. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756-795.

  • [11/17] 9. Association Analysis [download]

    • 1. Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1), 55-86.

      2. Geng, L., & Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3), 9-es.

  • [11/24] Review Session (Lectures 6-9, in Korean, optional)

  • [12/1] Final Exam

Supplementary Materials

• • [12/8] Special Topics in Data Mining - Invited Talk by Hyungu Kang (Ph.D. Student, SKKU)