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Data Mining: Potentials and Challenges

Data Mining: Potentials and Challenges. Rakesh Agrawal & Jeff Ullman. Observations. Transfer of data mining research into deployed applications and commercial products Greater success in vertical applications Horizontal tools: Examples:

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Data Mining: Potentials and Challenges

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  1. Data Mining:Potentials and Challenges Rakesh Agrawal & Jeff Ullman

  2. Observations • Transfer of data mining research into deployed applications and commercial products • Greater success in vertical applications • Horizontal tools: Examples: • SAS Enterprise Miner: Sophisticated Statisticians segment • DB2 Intelligent Miner: database applications requiring mining • Emergence of the application of data mining in non-conventional domains • Combination of structured and unstructured data • New challenges due to security/privacy concerns • DARPA initiative to fund data mining research

  3. Identifying Social Links Using Association Rules Input: Crawl of about 1 million pages

  4. Website Profiling using Classification Input: Example pages for each category during training

  5. Discovering Trends Using Sequential Patterns & Shape Queries Input: i) patent database ii) shape of interest

  6. Discovering Micro-communities Frequently co-cited pages are related. Pages with large bibliographic overlap are related.

  7. New Challenges • Privacy-preserving data mining • Data mining over compartmentalized databases

  8. 30 | 25K | … 50 | 40K | … Randomizer Randomizer 65 | 50K | … 35 | 60K | … Reconstruct Age Distribution Reconstruct Salary Distribution Decision Tree Algorithm Model Inducing Classifiers over Privacy Preserved Numeric Data Alice’s age Alice’s salary John’s age 30 becomes 65 (30+35)

  9. Other recent work • Cryptographic approach to privacy-preserving data mining • Lindell & Pinkas, Crypto 2000 • Privacy-Preserving discovery of association rules • Vaidya & Clifton, KDD2002 • Evfimievski et. Al, KDD 2002 • Rizvi & Haritsa, VLDB 2002

  10. Computation over Compartmentalized Databases

  11. Some Hard Problems • Past may be a poor predictor of future • Abrupt changes • Wrong training examples • Actionable patterns (principled use of domain knowledge?) • Over-fitting vs. not missing the rare nuggets • Richer patterns • Simultaneous mining over multiple data types • When to use which algorithm? • Automatic, data-dependent selection of algorithm parameters

  12. Discussion • Should data mining be viewed as “rich’’ querying and “deeply’’ integrated with database systems? • Most of current work make little use of database functionality • Should analytics be an integral concern of database systems? • Issues in data mining over heterogeneous data repositories (Relationship to the heterogeneous systems discussion)

  13. Summary • Data mining has shown promise but needs much more further research We stand on the brink of great new answers, but even more, of great new questions -- Matt Ridley

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