1. Special Topics in Data Mining Applications Focus on: Text Mining
INFS-795
Spring GMU

2. General Info Instructor: Carlotta Domeniconi Office: S&T2, Rm 449
Phone: (703)
Office hours: Tue 4-6pm, or by appointment
Visit the class webpage often!

3. Course Format Lectures by the instructor; One midterm;
Paper presentations by students;
One project:
Project proposal;
Project presentation
Project paper;

4. Visit the class webpage often !!!
Important Dates
March 10: Project proposal due;
March 24: Midterm Exam;
March 31: Students’ presentations start;
May 12: Paper on the project due.
Visit the class webpage often !!!

5. The final grade is based on…
Midterm: 25%
Paper presentation: 15%
Project (proposal, presentation, paper): 50%
Participation in class and quizzes on papers presented: 10%

6. Course Overview Classification: Bayes decision theory
Density estimation; Discriminant analysis
Decision trees; Nearest neighbors
Curse of dimensionality
Dimensionality reduction:
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Support Vector Machines

7. Course Overview Clustering: Basics Distance measures K-means
Subspace clustering

8. Course Overview Text categorization: Document representation;
Latent semantic indexing;
Unsupervised and supervised feature selection;
Feature weighting;
Similarity measures;
Semantic distances;
Kernel methods;
Detecting Spam .

9. Course Overview Presentation/Discussion of papers Project proposals;
list of papers provided;
Project proposals;
Project presentations;
Paper on the project;

10. We will study and learn…
Fundamental principles and techniques in data mining / machine learning;
Problems that arise in
Document classification
Existing approaches in data mining to address these problems;
Their limitations;
Can we do better?

11. Some useful books

12. On Pattern Classification:
R. O. Duda, P. E. Hart, D. G. Stork,
“Pattern Classification”,
Second Edition, Wiley, 2001.

13. On Document Classification:
S. Chakrabarti, “Mining the Web: Discovering Knowledge from Hypertext Data”, Elsevier Science, 2003.
Thorsten Joachims, “Learning to Classify Text using Support Vector Machines”, Kluwer 2002.

14. On Text Retrieval:
M. Berry and M. Browne, “Understanding Search Engines. Mathematical Modeling and Text Retrieval”, SIAM, 1999.

15. On Statistical Learning:
T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning. Data Mining, Inference and Prediction”, Springer, (Last Print!)