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Data Mining for Libraries


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Data analysis to do service assessment in academic libraries. Web presentation for the State University of New York Librarians' Association.

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Data Mining for Libraries

  1. 1. Data Mining for Libraries: What are the Possibilities? Elaine M. Lasda Bergman, MLS Twitter: @ElaineLibrarian Subject Librarian for Social Welfare University at Albany, SUNY SUNYLA Midwinter Conference January 30, 2015
  2. 2. What is Data Mining?
  3. 3. Knowledge Discovery In Databases (KDD) Input data Data Preprocessing Data Mining Postprocessing Information Adapted from Tan, et al. (2006), p.3
  4. 4. A note about data collection • It’s the kicker: GIGO • Cleaning • Preprocessing
  5. 5. What is Weka?
  6. 6. Weka for Prediction Mackenzie, Ian:
  7. 7. Decision Tree From Weka
  8. 8. Did Student use Email/IM reference Did student Receive instruction 0 sessions 1-2 session Time between grad/undergrad 1-5 years 100% yes None 45% yes 5+ years 100% yes 3+ sessions Student’ s residency status On campus full time Off campus full time Part time Likelihood of graduate students using library resources based on survey questions Yes No
  9. 9. Weka for Classification
  10. 10. Animal Clusters
  11. 11. Weka for Association Analysis
  12. 12. Association Rules
  13. 13. (Anomaly Detection)
  14. 14. How Can Libraries Use Data Mining?
  15. 15. Circling Back: It All Starts With Data Collection
  16. 16. Questions? Me: Elaine Lasda Bergman, Subject Librarian for Social Welfare, University at Albany email: Twitter: @ElaineLibrarian Resources used: Tan, P. et al. (2006). Introduction to Data Mining. Boston: Pearson Education, Inc. Newton, et al. (2012). Your Statistical Consultant: Answers to Your Data Analysis Questions. Thousand Oaks: SAGE Publications. Two good Weka Tutorials: Data Mining for the Masses: