Page History: Data Mining (Fall, 2012)

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Page Revision: 2012/09/19 23:07

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Information

• Course Number: 081202B3
• To: M. Sc. students of Department of Computer Science and Technology, Nanjing University.
• Classroom: 221, Computer Science and Technology Building, Xianlin Campus
• Time: 16:00 -- 17:50, Wednesday
• Office Hour: 14:30 - 15:30, Wednesday (Rm 917)
• Text Book: D. Hand, H. Mannila, P. Smyth. Principles of Data Mining. MIT Press, MA:Cambridge, 2001.
• Main Reference Books:
  • J. Han, M. Kamber. Data Mining: Concepts and Techniques, 2nd edition. Morgan Kaufmann Publishers, 2006
  • I. H. Witten, E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition. Morgan Kaufmann Publishers, 2011
  • P.-N. Tan, M. Steinbach, V. Kumar. Introduction to Data Mining, Addison-Wesley, 2006.
  • E. Alpaydin. Introduction to Machine Learning, 2nd edition. The MIT Press, 2010.
• Grading: Final exam (30%) + assignment 1 (20%) + assignment 2 (15%) + assignment 3 (15%) + assignment 4 (20%)
• TA: Mr. Teng Zhang and Mr. Chao Qian

Assignments

Assignment 1: Write a report on data mining applications         Due on Sept. 26, 2012         TA page

Assignment 2: A classification task         Due on Oct. 17, 2012         TA page

Assignment 3: A clustering task         Due on Nov. 7, 2012         TA page

Assignment 4: Mining from a real-world data set         Due on Dec. 5, 2012         TA page

Lecture slides

  

9/12: Introduction (Download PDF)		Reading material: Z.-H. Zhou. Three perspectives of data mining. Artificial Intelligence, 2003, 143(1): 139-146. H.-P. Kriegel, et al. Future trends in data mining. Data Mining and Knowledge Discovery, 2007, 15(1): 87-97. Q. Yang and X. Wu. 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making, 2006, 5(4): 597-604.
9/19: Data, Measurements, and Visualization (Download PDF)		Reading material: M. C. F. de Oliveira and H. Levkowitz. From visual data exploration to visual data mining: A survey. IEEE TVCG, 2003, 9(3): 378-394. H. Liu, F. Hussain, C. L. Tan, and M. Dash. Discretization: An enabling technique. DMKD, 2002, 6(4): 393-423. J. Dougherty, R. Kohavi, M. Sahami. Supervised and unsupervised discretization of continuous features. In Proceedings of ICML'95, 194-202, Tahoe City, CA. X. Zhu and X. Wu. Class noise vs. attribute noise: A qualitative study of their impacts. AI Review, 2004, 22(3-4): 177-210. Link: A javascript for simple data visualization
9/26: (TBD)

Links

• Weka An open source (Java) machine learning/data mining algorithms software.

• R Project An open source platform for statistic computing using R script language.
  • Machine learning packages for R

• ACM SIGKDD The website of the ACM Special Interest Group on Knowledge Discovery and Data Mining.
  • ACM SIGKDD Explorations Newsletters A magazine of SIGKDD.

• KDnuggets A website for data mining resources.

Major academic venues

• Journals in data mining: DMKD, TKDD, TKDE, KAIS
• Journals in machine learning: MLJ, JMLR
• Journals in database: TODS, VLDBJ
• Journals in information retrieval: TOIS, IP&M, IRJ
• Journals in web search and mining: WWWJ, TOIT, TWeb
• Conferences in data mining: KDD, ICDM, SDM, ECML/PKDD, PAKDD
• Conferences in machine learning: ICML, NIPS, COLT
• Conferences in database: SIGMOD, VLDB, ICDE
• Conferences in information retrieval: SIGIR, CIKM
• Conferences in web search and mining: WWW, WSDM

The end