An Evaluation of Commercial Data Mining

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 Oracle9is 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/mi
  ning1.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 Excel4.37385831
• 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 Oracle9is Data Mining Suite

11
Install Software for Oracle

• Including JDeveloper
• May be extended to the installation of other
  commercial data mining suites eg.
• DB2s Intelligent Miner
• Informixs Data Mine

12
Investigate Oracle9is 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, Oracle9is 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

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• 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 PanelData Mining The
  Next Ten Years available at http//www.acm.org/sig
  s/sigkdd/explorations/issue5-2/pnl_10yrs_final1.pd
  f
• Oracle Website
• Oracle Magazine