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

PROF. CAROLINA RUIZ 

DUE DATE: Thursday November 3rd, 2016.

• Slides: Submit via myWPI by 2:00 pm.
• Written report: Hand in a hardcopy by the beginning of class (by 3:59 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: Use the Heart Disease Data Set. This dataset is available at the UCI Machine Learning Repository.
  • Use the dataset in the processed.cleveland.data file.
  • See a more detailed description of the dataset in the heart-disease.names file.
  • Before you run experiments, transform the dataset as follows:
    • All data values are provided as numeric in the dataset, even though most attributes are actually discrete (= nominal). For each of the following nominal attributes:

      2. #4 (sex)
      3. #9 (cp)
      6. #16 (fbs)
      7. #19 (restecg)
      9. #38 (exang)
      11. #41 (slope)
      12. #44 (ca)
      13. #51 (thal)
      14. #58 (num) 
      

      replace its values with more readable nominal names following the description provided in the dataset webpage. For example, for attribute "2. #4 sex", use male instead of value 1, and female instead of value 0. This will make your job reading and analyzing association rules much easier.
    • For numeric (= continuous) attributes:

      1. #3 (age)
      4. #10 (trestbps)
      5. #12 (chol)
      8. #32 (thalach)
      10. #40 (oldpeak)
      

      use supervised discretization to convert them to nominal attributes. Use attribute "14. #58 (num)" as the target attribute.

  • For classification association rules, use attribute "14. #58 (num)" as the target attribute.

  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.
    • Search the medical literature online to see if medical studies have shown associations among the attributes present in your association rules.

• 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 an association rules research paper that you find intriguing.