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Data Mining

Data Mining. David Eichmann School of Library and Information Science The University of Iowa. Why?. Given enough data represented through enough dimensions, we loose the ability to see the patterns. How?. Decision Trees Nearest Neighbor Clustering Neural Networks Rule Induction

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Data Mining

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  1. Data Mining David Eichmann School of Library and Information Science The University of Iowa

  2. Why? • Given enough data represented through enough dimensions, we loose the ability to see the patterns

  3. How? • Decision Trees • Nearest Neighbor Clustering • Neural Networks • Rule Induction • K-Means Clustering

  4. What is it? • The automated extraction of hidden predictive information from databases. • Key points • Automated • Hidden • Predictive

  5. The Typical Process

  6. Evaluation Criteria • Receiver Operating Characteristic Curves

  7. But Nobody Said We Had To Do MATH….

  8. Forms of Data • Structured • Databases • Forms • Semi-Structured • Tables on the Web • Bibliographic citations • Graphs & charts • Unstructured • Full text (e.g., journal articles, physician chart notes) • Images

  9. Text Mining • Corpus now is a collection of text artifacts • Full text when you’ve got it (e.g. newswire) • Metadata when you don’t (e.g. MEDLINE) • The trick then becomes extracting ‘interesting’ relationships between ‘interesting’ entities • Who killed who • Who works for who • Who makes what

  10. The Classic Entities • Persons • Organizations • Places (Geography) • Events

  11. A Newswire Example • APW19981001.0262 [Israel(0.271), Jonathan Pollard (0.153), Benjamin Netanyahu(0.102), Bill Clinton(0.102), United States(0.055), ...] • Persons • Bill Clinton (3) • Jonathan Pollard (8) • Moshe Fogel (2) • Benjamin Netanyahu (2) • Israeli Embassy (1) • Organizations • Cabinet (1) • Places • Israel (16) • United States (5) • Washington (2)

  12. In the Medical/Health Realm • UMLS an excellent framework • Organism • Chemical • Activity • Disease

  13. A MEDLINE Example • Document: 89316090 - Reconstructive surgery in Nicaragua • Provided MeSH Keywords • Human • Nicaragua • Z01.107.169.690 • Surgery, Plastic/* • G02.403.810.788 • Phrases • [Reconstructive, surgery] • [Nicaragua] • [letter] • MeSH Terms • Surgery (1) • G02.403.810.762 • Letter [Publication Type] (1) • Other Phrases • Reconstructive surgery (1)

  14. Concept Extraction Example • “Roman forces under Julius Caesar invade Britain.” (S (NP (NP Roman forces) (PP under (NP Julius Caesar))) (VP invade (NP Britain)) .) • Entity Attributes: • <organization Roman forces> • <person Julias Caesar> • <placename Britain> • Concepts: • <Roman forces - under - Julius Caesar> • <Roman forces - invade - Britain>

  15. And a Small Demo…

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