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An Evaluation of Commercial Data Mining

An Evaluation of Commercial Data Mining. Proposed and Presented by Emily Davis Supervisor: John Ebden. Statement of the Problem. An Evaluation of Commercial Data Mining Capabilities, for example Oracle9i’s Data Mining Suite. Background. Data mining is a relatively new offshoot of

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An Evaluation of Commercial Data Mining

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  1. An Evaluation of Commercial Data Mining Proposed and Presented by Emily Davis Supervisor: John Ebden

  2. Statement of the Problem • An Evaluation of Commercial Data Mining Capabilities, for example Oracle9i’s Data Mining Suite.

  3. Background Data mining is a relatively new offshoot of database technology which has arisen as a result of the ability of computers to: • Store vast quantities of data in data warehouses. • Implement ingenious algorithms for the mining of data. • Use these algorithms to analyse these vast quantities of data in a reasonable amount of time.

  4. Data mining discovers the patterns in data that represent knowledge. • It is of interest what algorithms data mining suites use and how well each category of data mining algorithm performs on data and what kind of results are produced. • Another important issue is usability of the algorithm. • Random Number Example taken from http://www.saltspring.com/brochmann/math/mining/mining1.html

  5. #           data a data b        data c 1.00000000 0.71132700 0.15379400 1.88403600 2.00000000 0.62219935 0.83119106 3.73797189 3.00000000 0.33872289 0.80881084 3.10387831 4.00000000 0.54262732 0.35427095 2.14806749 5.00000000 0.50631348 0.71599532 3.16061290 6.00000000 0.00132503 0.22447315 0.67606951 7.00000000 0.76211535 0.94620700 4.36285170 8.00000000 0.91026206 0.89499186 4.50549970 9.00000000 0.92640874 0.47156928 3.26752532 10.0000000 0.49323546 0.27673696 1.81668179 11.0000000 0.04501477 0.30142353 0.99430013 12.0000000 0.49180000 0.17909135 1.52087404 13.0000000 0.06747225 0.85629071 2.70381663 14.0000000 0.84239974 0.41916601 2.94229750

  6. 49.0000000 0.07845276 0.69584199 2.24443147 50.0000000 0.07548299 0.52973340 1.74016616 51.0000000 0.72301849 0.97594044 ???????? Data A and B random numbers generated in Excel. Data c = 2*(data a) + 3*(data b).

  7. 51st value calculated by Excel:4.37385831 • Value calculated using Knowledge Miner – a Macintosh data mining tool: 4.34791231 and the equation : 1.97*(data a) + 2.96*(data b) + 0.0324

  8. Experiment repeated using three columns of random numbers and this equation: Data d = 23*(data a)-4.5*(data b)+(data a + data c) . • The last five entries for Data D were missing from the column.

  9. These were generated by Excel: 14.7314558 12.0720505 22.0008992 7.52633344 5.25167700 • These are what Knowledge Miner predicted: 14.7341613 12.0731391 22.0080223 7.52465867 5.24861860

  10. Plan of Action • Literature Survey (and other resources) • Install Software for Oracle • Get to know the Oracle Suite • Evaluate Oracle9i’s Data Mining Suite

  11. Install Software for Oracle • Including JDeveloper • May be extended to the installation of other commercial data mining suites eg. DB2’s Intelligent Miner Informix’s Data Mine

  12. Investigate Oracle9i’s Data Mining Suite • Two major algorithm types – supervised and unsupervised learning. • A Medical Example: Supervised learning – researchers input medical profiles into a leukaemia model to predict propensity for the disease. Unsupervised learning – searches for clusters of related information in data sets to reveal insights about diseases and patient populations.

  13. Get to know the Oracle DM Suite (a major task). • Explore JDeveloper, Oracle9i’s Java based API. • JDeveloper complies with JDM (Java Data Mining) used by Oracle, Sun, IBM and others. • Explore DM4J( Data Mining for Java) the new Graphical User Interface for Oracle DM.

  14. Addressing the Problem: • Run the different algorithms available in the data mining suite. • Document and analyse results in terms of performance and effectiveness of algorithm.

  15. Expected Results: • The ability to say conclusively whether Oracle's data mining capabilities are inferior or superior to anything else in the market place and why this can be stated.

  16. Possible Extensions to the Project: • To have sufficient knowledge of the topic to give recommendations or feedback: • to Oracle regarding their data mining suite. • to IT customers wanting to purchase data mining suites. • Explore the field of Random stereograms- could a computer see them? If not, why not?

  17. Literature Survey • Principles of data mining by David Hand, Heikki Mannila and Padhraic Smyth, Cambridge Massachusetts, MIT Press, 2001 – algorithmic concepts • Data mining: concepts and techniques by Jiawei Han and Micheline Kamber, San Francisco, California, Morgan Kauffmann, 2001 – algorithmic evaluations • Data mining: a tutorial- based primer by Richard J. Roiger and Michael W. Geatz, Boston, Massachusetts, Addison Wesley, 2003 - practical knowledge and processing

  18. Data Mining by Pieter Adriaans and Dolf Zantinge, Harlow, England, Addison Wesley, 1996 – real life application • Data Mining and Statistical Analysis Using SQL by Robert P. Trueblood and John N. Lovett, Jnr., USA, Apress, 2001 – statistical principles • Data Mining Using SAS Applications by George Fernandez, USA, Chapman and Hall/CRC, 2003 - methodologies

  19. Mastering Data Mining: The Art and Science of Customer Relationship Management by Michael J.A. Berry and Gordon S. Linoff, USA, Wiley Computer Publishing, 2000 – building effective models • Data Preparation for Data Mining by Dorian Pyle, San Francisco, California, Morgan Kauffman, 2000 – Demo code, 10 Golden Rules.

  20. The White Paper: Data Mining- Beyond Algorithms by Dr Akeel Al-Attar, available at http://www.attar.com/tutor/mining.htm • Summary from the KDD-03 Panel—Data Mining: The Next Ten Years available at http://www.acm.org/sigs/sigkdd/explorations/issue5-2/pnl_10yrs_final1.pdf • Oracle Website • Oracle Magazine

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