Data Mining (Graduate, 2015)

Textbook

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

Final Exam

2016年1月6日下午2点-4点，仙1-106、仙1-107，闭卷

Assignments

Please read carefully the assignments in http://lamda.nju.edu.cn/qianh/dm15.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

  3. Similarity and Distances
    Reference: Chapter 3 of the Textbook

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

  5. Cluster Analysis: Part A
  6. 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.

  7. Outlier Analysis
    Reference: Chapter 8 of the Textbook

  8. Data Classification: Part A
  9. Data Classification: Part B
    Reference: Chapter 10 of the Textbook
               Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2): 121-167, 1998.
               Shalev-Shwartz et al. Pegasos: primal estimated sub-gradient solver for SVM. In ICML, 807-814, 2007.

  10. 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.

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

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

  13. Mining Text Data
    Reference: Chapter 13 of the Textbook

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

  15. Mining Big Data
    Reference: Hazan and Kale. Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization. In COLT, 421-436, 2011.
               Boyd et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends in Machine Learning, 3(1): 1-122, 2010.
               Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In ICML, 928-936, 2003.
               Hazan et al. Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2-3): 169-192, 2007.