Instructor

Dr. Tao Li, Assistant Professor
School of Computer Science and Engineering
Florida International University

Office: ECS 318
Email: taoli AT cs.fiu.edu
Office Hours: Tuesday 2:30pm-4:30pm or by appointment

Meeting Time and Location

Tuesday 6:25pm-9:05pm, ECS 235

Course Materials

• Lecture Notes and Assignments

Course Description

Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This course is designed to give a graduate-level student an introductory survey to the methodologies, technologies, mathematics and algorithms currently needed by people who do research in data mining or who may need to apply data mining techniques to practical applications. Emphasis will be laid on both algorithmic and application issues.

Course Syllabus (Subject to revision)

• Mathematical Background for Data Mining
  • Probability Theory
  • Information Theory
  • Basic Linear Algebra
  • Expectation and Maximization
• Association Mining
  • Frequent Set Mining
  • Sequence Mining
• Classification
  • Decision Tree Learning
  • Nearest Neighbor
  • Support Vector Machines
  • Bayesian Networks, Maximum Likelihood, Maximum Entropy
  • Feature Selection and Dimension Reduction
• Clustering
  • Traditional approaches (e.g., K-means, Hierarchical etc.)
  • Spectral Clustering, Matrix Factorization
  • Subspace Clustering
  • Co-clustering
• Ensemble Methods
  • Classifier Combination
  • Cluster Combination
• Web Mining
  • Web Content Analysis
  • Discovering web communities
  • Pagerank, Hub and Authorities
  • Web Crawling
• Optional Topics
  • Privacy issues in data mining
  • Similarity search
  • Basics of natural language processing

Prerequisites

Basically students need to know at least a programming language (e.g., C/C++, Java or Matlab etc.). Students entering the class with basic knowledge of probability, statistics and algorithms will be at an advantage, but the class will be designed so that anyone with basic mathematical background can catch up and fully participate.

Format and Grading

The course assignments include projects, written homeworks, paper discussions and presentations. Research projects will be designed to improve the critical analysis and problem-solving skills of students. Class attendance is mandatory. In addition, occasional quizzes will be given in class. Evaluation will be a subjective process, but it will be primarily based on the students' understanding of the course material. Final grades will be calculated as follows.

Quizzes	15%
Class Participation	10%
Exams	20%
Assignments	55%

Policies on Assignments and Exams

Misc Links

• jobs in data mining listed at KDnuggets
• Data Mining Software links by Dr Pang-Ning Tan
• Weka
• Rapid Miner

Textbooks and References

Textbook

• Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Introduction to Data Mining. Addison Wesley, 2005.

References

• Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006, Second Edition.
• Tom Mitchell. Machine Learning. McGraw Hill, 1997.
• Hastie, Tibshirani and Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.
• Chakrabarti. Mining the Web: discovering knowledge from hypertext data. Morgan Kaufmann , 2003. Available on line at FIU Library .

A lot of reading material from top conferences/journals will be made available online or in class as required. In addition, lecture notes will be available on line.

Code of Academic Integrity:

http://www.fiu.edu/~oabp/misconductweb/2codeofacainteg.htm

University Policies:

• http://www.fiu.edu/provost/polman/sec2/sec2web2-44.htm
• http://www.fiu.edu/~eop/EOPSexH.pdf
• http://www.fiu.edu/~provost/polman/sec19web.html