Data Mining (Graduate, 2015)
Charu C. Aggarwal. Data Mining: The Textbook, Springer, May 2015.
Please read carefully the assignments in http://lamda.nju.edu.cn/qianh/dm15.html, and accomplish them in time.
- 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.
- Data Preparation
Reference: Chapter 2 of the Textbook
- Similarity and Distances
Reference: Chapter 3 of the Textbook
- Association Pattern Mining
Reference: Chapter 4 of the Textbook
- Cluster Analysis: Part A
- 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.
- Outlier Analysis
Reference: Chapter 8 of the Textbook
- Data Classification: Part A
- 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.
- 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.
- Data Classification: Advanced Concepts
Reference: Chapter 11 of the Textbook
- Linear Methods for Regression
Reference: Chapter 3 of Hastie, Tibshirani and Vandenberghe. The Elements of Statistical Learning. Springer, 2009.
- Mining Text Data
Reference: Chapter 13 of the Textbook
- Mining Web Data
Reference: Chapter 18 of the Textbook
- 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.