CS 548 KNOWLEDGE DISCOVERY AND DATA MINING - Spring 2016  
Project 3: Association Rule Mining

PROF. CAROLINA RUIZ 

DUE DATE: Tuesday, March 22nd, 2016.

• Slides: Submit via myWPI by 4:00 pm.
• Written report: Hand in a hardcopy by the beginning of class (by 5:59 pm).

Project Assignment

• Read sections 6.1-6.3, 6.7-6.9 of the textbook in great detail.

• Study all the materials posted on the course Lecture Notes:
  • Association Rules
  In particular, you should know what an association rule is; metrics to quantify association rules (e.g., support, confidence, lift, leverage, conviction, interest factor, correlation analysis, IS measure, ...); the Apriori principle; the Apriori algorithms to construct association rules, including frequent itemset generation and candidate generation and prunning (join/merge condition and subset pruning), and rule generation and confidence-based pruning. You should be able to use these algorithms to construct association rules from data by hand during the test. See examples provided in the Lecture Notes linked above.

• THOROUGHLY READ AND FOLLOW THE PROJECT GUIDELINES. These guidelines contain detailed information about how to structure your project, and how to prepare your written summary, and how to study for the test.

  *** You must use the Project 3 Template provided for your written report. (If you prefer not to use Word, you can copy and paste this format in a different editor as long as you respect the stated page structure and page limit.)

• Data Mining Technique(s): We will run experiments in Weka and in Python using the following techniques:
  • Pre-processing Techniques: Feature selection, feature creation, dimensionality reduction, noise reduction, attribute discretization, ...
  • Association rule mining: Apriori algorithm (both for association rules and for classification association rules) available in Weka and in Python.

• Dataset(s):

  • Students taking CS548: Use the Bank Marketing Data Set (use the bank-additional-full.csv data file). This dataset is available at the UCI Machine Learning Repository.

    • For classification association rules, use attribute y (that is, attribute #21) as the target attribute.

  • Students taking BCB503: Use the Mice Protein Expression Data Set. This dataset is available at the UCI Machine Learning Repository.

    • For classification association rules, use attribute Class (that is, attribute #82) as the target attribute. The large number of values of this attribute (8) may make the classification task hard. Run preliminary experiments to decide whether to use this attribute as the target or to convert it into a binary attribute (e.g., stimulated to learn vs. not stimulated to learn; or injected with saline vs. injected with memantine) or anothe reasonable option.

  Run experiments with and without data pre-processing to determine what pre-processing produces useful and meaningful association rules.

• Evaluation:
  • Quantitative evaluation:
    • Use support, confidence, lift, leverage, and conviction. Include in your report a definition (using a precise formula) and a description of the meaning of each of these metrics.
    • Also, for extra credit you are encouraged (but not required) to implement in Weka other association rule metrics defined in Section 6.7 of the textbook (e.g., interest factor, correlation analysis, IS measure, ...), and experiment with them.
  • Qualitative evaluation:
    • Use visualizations of the sets of association rules obtained and analyze those visualitions.
    • Read the association rules obtained and pick a handful of interesting ones to describe in your report.

• General Comments: In constrast with our previous classification and regression projects, we won't use any evaluation protocol (e.g., 10-fold cross validation) for the association analysis of this project, as we're not using the rules for prediction. Focus instead on experimenting with different ways of preprocessing the data, varying the parameters of the Apriori algorithm, and providing your own method to evaluate the resulting collections of association rules. Remember to experiment with car (that is, classification association rules) and to compare its classification performance to that of decision trees; and remember to experiment with non-car rules also.

• Advanced Topic(s): Investigate in more depth (experimentally, theoretically, or both) a topic of your choice that is related to association rule mining and that is not covered already in this project. This association rule mining -related topic might be something that was described or mentioned in the textbook or in class, or that comes from your own research, or that is related to your interests, or that appears in a research paper that you find intriguing.