CS 548 KNOWLEDGE DISCOVERY AND DATA MINING - Spring 2014  
Project 3: Association Rule Mining

PROF. CAROLINA RUIZ 

DUE DATE: Friday, March 28th, 2014.

• Written Report: Hand in a hardcopy BY the beginning of class 3:00 pm.
• Class Discussion and Test during class that day.

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 follow the 5 page written report format described in the PROJECT GUIDELINES. In particular for this project:
  Page 2 should contain a table summarizing association rule mining experiments.
  Page 3 should contain a table summarizing classification association rule mining experiments.

  *** 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 RapidMiner using the following techniques:
  • Pre-processing Techniques: Feature selection, feature creation, dimensionality reduction, noise reduction, attribute discretization, ...
  • Association rule mining: all the association rule mining techniques available in Weka and in RapidMiner.

• Dataset(s): In this project, we will use two datasets (about 25% of the experiments should be done with the 1st dataset and about 75% with the 2nd dataset):

  • The Congessional Voting Records dataset, available in Weka as vote.arff. There is a description of this dataset in Section 6.3.3 of your textbook, and at Congressional Voting Records Dataset of the UCI Machine Learning Repository.

  • The NSF Research Award Abstracts 1990-2003 Data Set available at the UCI Machine Learning Repository.
    Get very familiar with this dataset. This dataset will provide you with some experience working with text data. For the project use the following file:
    • nsfabs_part1_out.zip
    Convert the contents of this zip file into an .arff file that contains a row for each document. Then replace each word number with the corresponding word as given in the file:
    • words.zip
    before you mind association rules. That way it will be easier to interpret the mined rules.

• Performance Metric(s): 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.

• 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 addition to non-car rules.

• 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.