Association Rules

Task 1.

This involves:
1. You will use any data (You can use any data from the link in Task2).
2. Preparing and preprocessing the data to be used in WEKA.
3. Finding rules, including appropriate parameter setting.
4. Determining which of the resulting rules are interesting.
5. Figuring out how the interesting rules could be useful.

Task 2.

Go to the following repository:

The UCI Machine Learning Repository

http://www.ics.uci.edu/~mlearn/MLRepository.html

This contains many data sets, not all of which are appropriate for association rules, so you'll need to do some thinking and discuss one set which can be used for mining association rules. You are also welcome to identify data from other sources, especially those that you find personally of interest. Put this discussion in you report.

Project Report

The project report should contain the following:

1. Objectives: What is the domain and what are the potential benefits to be derived from association rule mining. This is high level - not find patterns, but what would improve because of the use of the patterns.

2. Data set description: What is in the data and what preprocessing was done to make it amenable for association rule mining. Where choices were made (e.g. decisions to ignore an attribute), describe your reasoning behind the choices.

3. Rule mining process: Parameter settings, choice of algorithm (implement WEKA-provided A priori).

4. Resulting rules: Summary (number of rules, general description), and a selection of those you would show to a client.

Ten minutes presentation from each one.