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Enhancing Precision in Rule Mining from Surveys and Questionnaires

Discover how to improve precision in data mining from surveys and questionnaires through techniques like clustering, pruning, and handling ordinal/Likert data. Learn from real datasets and explore future work areas.

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Enhancing Precision in Rule Mining from Surveys and Questionnaires

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  1. Mining Rules from Surveys and Questionnaires Scott Burton and Richard Morris CS 676 Presentation 12 April 2011

  2. Surveys and Questionnaires • Frequently Used • Problems for data mining • Rarity • Related and dependent questions • Ordinal / Likert scale

  3. Association Rule Mining Market basket analysis Cookies -> Milk

  4. Our Goal: Improve Precision Standard Algorithms/Approaches • Apriori, MS-Apriori • Too many rules • Rules are not “interesting” or actionable • Finding the needle in the haystack Our goal • Improve Precision • How do you measure “interestingness?”

  5. Interestingness Measures • Mostly based on Support or Confidence • Considered about 40 different metrics • All seemed to favor the wrong types of rules

  6. Our Datasets • Smoking habits of middle school students in Mexico • Global Youth Tobacco Survey for the Pan American Health Organization (GYTSPAHO) • ~65 Questions and 13,000 responses • HINTS (Health Information National Trends Survey) • hints.cancer.gov • 2007 response data had ~475 Questions and 8,000 responses • We focused on a subset of ~100 questions

  7. Apriori vs. MS-Apriori Apriori (Figure 1) MS-Apriori (Figure 2)

  8. Related and Dependent Questions True but worthless rules • Do you smoke=no -> Did you smoke last week=no Our approach • Cluster similar questions • Remove any intra-cluster rules 1 2 3 7 4 8 9 5 6

  9. Creating Clusters • Distance Metrics • Bi-conditional prediction • Attribute vs. Attribute-Value pair • Involving the subject matter expert

  10. A Sample Clustering of Questions (see handout)

  11. Effects of Cluster Pruning MS-Apriori (Figure 2) After cluster pruning (Figure 3)

  12. Similar Rules Abstract Viewpoint: • A B -> C D • A -> C D • A B -> C • A B Z -> C D

  13. Similar Rule Pruning

  14. Effects of Similar Rule Pruning After cluster pruning (Figure 3) After Similar Rule Pruning (Figure 4)

  15. Ordinal and Likert Data Two Approaches • Pre-process • Post-process Likert Ordinal

  16. Effects of Pre-Binning (Figure 5)

  17. Other Examples • HINTS Data (see handout, Figures 6-10)

  18. Conclusions and Future Work Conclusions • Increased precision of “interesting” rules • More work to be done Future work • Tuning of existing processes • Handle numerical data • Handle questions not asked to everyone • Handle questions with multiple responses • Try other record matching techniques for similar rule pruning

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