CS 548 KNOWLEDGE DISCOVERY AND DATA MINING - Spring 2012  
Project 5: Advanced Data Mining Applications

PROF. CAROLINA RUIZ 

DUE DATE: Thursday April 26th and Monday April 30th, 2012.

• Oral Presentation: during class Thursday April 26th or Monday April 30th.
• Slides: Submit by email by 10:00 am of the same day you are presenting.
• Written report: Hand in a hardcopy by 1:00 pm on Monday April 30th.

Project Description

• Project Instructions: THOROUGHLY READ AND FOLLOW THE PROJECT GUIDELINES. These guidelines contain detailed information about how to structure your project, and how to prepare your written and oral reports.

  Your written report should be at most 10 pages long (including all the graphs, figures, and appendices). The font size should be no smaller than 11pts.

• Data Mining Technique(s): Choose ONE of the following topics for your project:
  • anomaly detection
  • web mining
  • text mining
  • sequence mining
  • multimedia data mining

• Dataset: Choose a dataset appropriate for the data mining topic that you selected for this project and related to your own interests. This dataset should contain enough instances and attributes to provide sufficient data for fruitful and interesting experiments.
  Here are some possibilites:
  • A dataset you are working with for your research or your job.
  • A dataset from a data repository listed in the online resources, or other online data repository.
  • Other data source of your choice.

• Performance Metric(s): Use performance metrics appropriate to the mining application that you chose. If you are not aware of any, propose a variety of approaches to measure how good the results of your experiments are. Consider using visualization of the constructed model or patterns to evaluate your results. The more creative/ingenious your approaches, the better. You might want to extend the Weka code to provide the evaluation/interpretation functionality you need.

• General Comments
  • Focus on experimenting with different ways of preprocessing the data, adapting different techniques studied in this course to tackle the problem at hand, and investigating on your own other existing approaches. The more creative/ingenious your work and/or the more research into the related literature you do, the better.
  • Extra credit will be given to particularly creative and/or high quality work, and/or for independently researching the data mining chosen beyond what was covered in class.

• Project 5 Grading Sheet