Data Mining Research Papers Ppt


Data Mining Paper Presentations

Spring 2018

Please refer to the Research Guide for information about what to say in a good talk.
If a paper/book does not yet have PPT slides, you will need to generate new slides.

    Database Specialty

  1. J. Han, J. Pei, and Y. Yin, Mining Frequent Patterns without Candidate Generation, Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'00), Dallas, TX, May 2000.
    Presenter: Xindong Wu
    Presentation Date: April 10, 75 min
    2014 & 2016 presentation slides in PowerPoint: Here.
  2. R. Srikant and R. Agrawal, Quantitative Association Rules, Proc. of the ACM SIGMOD Int'l Conference on Management of Data, 1996.
    Presenter: Sahan Ahmad
    Spring 2013 Presenter: Sepehr Amir-Mohammadian
    2013 Presentation slides in PowerPoint: Here.
  3. Xindong Wu, Chengqi Zhang, and Shichao Zhang, Efficient Mining of Both Positive and Negative Association Rules, ACM Transactions on Information Systems, 22(2004), 3: 381-405.
    Presenter: Titli Sarkar
    Presenter: Michael Tripp
    2014 Presentation slides in PowerPoint: Here.
  4. J. Han and Y. Fu, Multiple-Level Association Rules, IEEE Transactions on Knowledge and Data Engineering, Volume 11, Number 5, October 1999.
    Presenter: Brennan P Guidry
    Spring 2016 Presenter: Chris Hutchinson
    2016 presentation slides in PowerPoint: Here.
  5. Chun-Nan Hsu and Graig A. Knoblock, Discovering Robust Knowledge from Databases that Change, Data Mining and Knowledge Discovery, Volume 2, Issue 1, 1998, 69-95.
    Presenter: Oyetokunbo Ipaye
    Presenter: Danielle Steimke
    2014 Presentation slides in PowerPoint: Here.
  6. S.D. Lee, David Cheung and Ben Kao, Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules, Data Mining and Knowledge Discovery, Volume 2, Issue 3, 1998, 233-262.
    Presenter: Xiaoguang Xiao
    Presenter: Matthew Starbuck
    2014 Presentation slides in PowerPoint: Here.
  7. Mohammed J. Zaki, Efficiently Mining Frequent Trees in a Forest, KDD 2002.
    Presenter: Prisca Egbua
    Spring 2016 Presenter: Prajwal Shrestha
    2015 & 2016 Presentation slides in PowerPoint: Here.
  8. Pedro Domingos and Geoff Hulten, Mining High-Speed Data Streams, KDD 2000.
    Presenter: Akintunde Tolu Jemiseye
    Presenter: Mounika Pylla
    2017 Presentation slides in PowerPoint: Here.
  9. M. Hernandez and S. Stolfo, Real-World Data is Dirty: Data Cleansing and The Merge/Purge Problem, Data Mining and Knowledge Discovery, Volume 2, Issue 1, 1998, 9-37.
    Presenter: Ayokomi Lasisi
    Spring 2016 Presenter: Jeff Maynard
    2016 Presentation slides in PowerPoint: Here.
  10. Xifeng Yan and Jiawei Han, gSpan: Graph-Based Substructure Pattern Mining, ICDM 2002.
    Presenter: Xuan Li
    Presenter: Yi He
    2017 Presentation slides in PowerPoint: Here.

    AI Specialty

  11. Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), 607-616.
    Presenter: Nishanth Anandanadarajah
    Presenter: Ashifur Rahman
    2017 Presentation slides in PowerPoint: Here.
  12. R. Agrawal and R. Srikant, Mining Sequential Patterns, Proc. of the Int'l Conference on Data Engineering (ICDE), Taipei, Taiwan, March 1995.
    Presenter: Marcus Shannon
    Spring 2016 Presenter: Julie Daly
    2016 Presentation slides in PowerPoint: Here.
  13. Freund, Y. and Schapire, R. E. 1997. A Decision-Theoretic Generalization of On-Line Learning and An Application to Boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.
    Presenter: Razin Hussain
    Presenter: Steven Olson
    2015 Presentation slides in PowerPoint: Here.
  14. Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the Seventh international Conference on World Wide Web (WWW-7) (Brisbane, Australia). P. H. Enslow and A. Ellis, Eds. Elsevier Science Publishers B. V., Amsterdam, The Netherlands, 107-117.
    Presenter: Xindong Wu
    Presenter: Abdullah Nur
    Presentation Date: April 17, 75 min
    2017 presentation slides in PowerPoint: Here.
  15. Xindong Wu, Fuzzy Interpretation of Discretized Intervals, IEEE Transactions on Fuzzy Systems, Volume 7, Number 6, 1999, 753-759.
    Presenter: Drew Richard
    Spring 2006 Presenter: Peter Duval
    2006 Presentation slides in PowerPoint: Here.
  16. Tom Fawcett and Foster Provost, Data Mining for Adaptive Fraud Detection, Data Mining and Knowledge Discovery, Volume 1, Issue 3, 1997, 291-316.
    Presenter: SM Zobaed
    Spring 2016 Presenter: Eric Dewind
    2016 Presentation slides in PowerPoint: Here.
  17. Xindong Wu, Fei Xie, Gongqing Wu, Wei Ding, PNFS: Personalized Web News Filtering and Summarization, International Journal of Artificial Intelligence Tools, 22(2013), Issue 5.
    Presenter: Clark Wagener
    Presenter: Rashida Hasan
    2017 Presentation slides in PowerPoint: Here.
  18. R. Kosala and H. Blockeel, Web Mining Research: A Survey, SIGKDD Explorations, June 2000. Volume 2, Issue 1.
    Presenter: Jesse Broussard
    Presenter: Ryan Patterson
    2014 Presentation slides in PowerPoint: Here.

    Added Papers for 2018

    • J. Dorre, P. Gerstl, and R. Seiffert, Text Mining: Finding Nuggets in Mountains of Textual Data, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, August 15-18, 1999, 398-401.
      Jeffrey Laborde
      Presenter: Trevor Crum
      2014 Presentation slides in PowerPoint: Here.

    Statistics Specialty

  19. Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc.
    Presenter: Xindong Wu
    Presentation Date: April 19, 75 min
    Presenter: Boisy Pitre
    2017 Presentation slides in PowerPoint: Here.
  20. McLachlan, G. and Peel, D. (2000). Finite Mixture Models + Streaming Features. J. Wiley, New York.
    Presenter: Jeevithan Alagurajah (Streaming Features)
    Presenter: Ege Beyazit (EM Algorithm); Presentation Date: April 12, 75 min
    Presenter: Ege Beyazit
    2017 Presentation slides in PowerPoint: Here.
  21. Douglas Fisher, Iterative Optimization and Simplification of Hierarchical Clusterings, Journal of Artificial Intelligence Research, 4(1996), 147-180.
    Presenter: Tingting Yang
    Spring 2007 Presenter: Paul Haake
    2007 Presentation slides in PowerPoint: Here.
  22. David Heckerman, Bayesian Networks for Data Mining, Data Mining and Knowledge Discovery, Volume 1, Issue 1, 1997.
    Presenter: Nazmul Shahadat
    Spring 2016 Presenter: Dilan Kiley
    2016 Presentation slides in PowerPoint: Here.
  23. Tian Zhang, Raghu Ramakrishnan, Miron Livny, BIRCH: A New Data Clustering Algorithm and Its Applications, Data Mining and Knowledge Discovery, Volume 1, Issue 2, 1997, 141-182.
    Presenter: Ming Sun
    Spring 2009 Presenter: Zhao Li
    2009 Presentation slides in PowerPoint: Here.
  24. Stephen L. Salzberg, On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach, Data Mining and Knowledge Discovery, Volume 1, Issue 3, 1997, 317-328.
    Presenter: Yilang Guo
    Presenter: Jiyeon Kim
    2014 Presentation slides in PowerPoint: Here.

Please e-mail queries and comments to xwu@louisiana.edu.

Jiawei Han, MichelineKamber and Jian Pei

Data Mining: Concepts and Techniques, 3rded.

The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791

Slides in PowerPoint

Chapter 1. Introduction

Chapter 2. Know Your Data

Chapter 3. Data Preprocessing

Chapter 4. Data Warehousing and On-Line Analytical Processing

Chapter 5. Data Cube Technology

Chapter 6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts andMethods

Chapter 7. Advanced Frequent Pattern Mining

Chapter 8. Classification: Basic Concepts

Chapter 9. Classification: Advanced Methods

Chapter 10. Cluster Analysis: Basic Concepts and Methods

Chapter 11. Cluster Analysis: Advanced Methods

Chapter 12. Outlier Detection

Chapter 13. Trends and Research Frontiers in Data Mining

Updated Slides for CS, UIUC Teaching in PowerPoint form

(Note: This set of slides corresponds to the current teaching of the data mining course at CS, UIUC.  In general, it takes new technical materials from recent research papers but shrinks some materials of the textbook.  It has also re-arranged the order of presentation for some technical materials.)

Instructions on finding the new sets of slides are as follows:

1.      Go to the homepage of the first author, Prof. Jiawei Han: http://web.engr.illinois.edu/~hanj/

2.      Click the following links in the section of Teaching:

a.      UIUC CS412: An Introduction to Data Warehousing and Data Mining 

b.      UIUC CS512: Data Mining: Principles and Algorithms

3.      Download the slides of the corresponding chapters you are interested in

Back to Data Mining: Concepts and Techniques, 3rded.

Back to Jiawei Han, Data and Information Systems Research Laboratory, Computer Science, University of Illinois at Urbana-Champaign

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