Data Mining (Graduate, 2016)

Final Exam

2016年12月28日（周三）下午2点-4点，逸A-117、逸B-101，闭卷

Textbook

Charu C. Aggarwal. Data Mining: The Textbook, Springer, May 2015.

Assignments

Please read carefully the assignments in http://lamda.nju.edu.cn/yehj/DM16/dm16.html, and accomplish them in time.

Slides

  1. Introduction to Data Mining
    Mathematical Background (Learn by yourself)
    Reference: Chapter 1 of the Textbook
               Petersen and Pedersen. The Matrix Cookbook. Technical University of Denmark, 2012.
               Appendices of Boyd and Vandenberghe. Convex Optimization. Cambridge University Press, 2004.

  2. Data Preparation
    Reference: Chapter 2 of the Textbook
               Dan Kalman. A Singularly Valuable Decomposition: The SVD of a Matrix. The American University, 2012.

  3. Similarity and Distances
    Reference: Chapter 3 of the Textbook
               Tenenbaum et al. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500): 2319-2323, 2000.

  4. Association Pattern Mining
    Reference: Chapter 4 of the Textbook

  5. Machine Learning for Big Data by Wu-Jun Li

  6. Cluster Analysis: Part A
  7. Cluster Analysis: Part B
    Reference: Chapter 6 of the Textbook
               Belkin and Niyogi. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. In NIPS 14, 2001.
               Lee and Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401: 788-791 1999.
               Xu et al. Document clustering based on non-negative matrix factorization. In SIGIR, 2003.

  8. Outlier Analysis
    Reference: Chapter 8 of the Textbook

  9. Data Classification: Part A
  10. Data Classification: Part B
    Reference: Chapter 10 of the Textbook

  11. Convex Optimization
    Reference: Boyd and Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
               Nesterov. Gradient methods for minimizing composite functions. Mathematical Programming, 140(1): 125-161, 2013.
               Hazan and Kale. Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. In COLT, 421-436, 2011.

  12. Data Classification: Advanced Concepts
    Reference: Chapter 11 of the Textbook

  13. Linear Methods for Regression
    Reference: Chapter 3 of Hastie, Tibshirani and Vandenberghe. The Elements of Statistical Learning. Springer, 2009.

  14. Mining Web Data
    Reference: Chapter 18 of the Textbook