Data Mining

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Find a dataset suitable for association rule mining and use Orange, Weka, or IPython Notebook to find interesting association rules. You can also use the Java-based SPMF tool:
With SPMF, you can try doing more advanced analysis, such as using multiple-supports and sequential pattern mining.

Make sure the data you find is in a suitable format. Generate the association rules and rank them by the various metrics such as support, confidence, lift, and others. Try to identify the most interesting, useful, and surprising rules based on the combinations of the metrics.

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Describe the data, methodology, and results in a formal technical report. Make sure to analyze the results and describe the implications of the rules you have found. Discuss whether they follow from intuition and could they generalize to unseen data.

Use the attached template. Make sure to include figures and tables that describe the process and the outcomes, and reference them from the text. Submit your report using a PDF document format.

Grading Rubric (25 points total):
0-5: Data (suitable for problem, sufficiently large, non-trivial)
0-5: Methodology (appropriate methods and metrics used)
0-5: Results (non-trivial, interesting, data-driven results)
0-5: Presentation (well written report, good use of figures and tables, used references when appropriate, no spelling or grammar mistakes)
0-5: Following directions (submission format, software used, etc.)

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