Data Mining with Oracle using Classification and Clustering Algorithms

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Data Mining with Oracle using Classification and
Clustering Algorithms

• Proposed and Presented by
• Nhamo Mdzingwa
• Supervisor John Ebden

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Presentation Outline

• Problem Statement
• Objective
• Background
• Expected Results
• Possible Extensions
• Plan of action
• Timeline
• Literature Survey
• Questions

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Problem Statement

• The commercial world is fast reacting to the
  growth potential in the DM area, as a wide
  range of tools are being marketed as DM suites.
• Examples of these are
• Oracle DM
• DB2s Intelligent Miner
• Informixs Data Mine
• SQL Data miner
• Ghost miner
• Clementine 9.0 (SPSS)
• SAS
• Gornish systems, etc

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Problem

• It is vital to know the algorithms a DM suite
  uses and which algorithm to use on a particular
  data set.
• Secondly, how well each algorithm performs in
  terms of accuracy, efficiency and effectiveness
  when using a particular DM suite e.g. Oracle DM.

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Objective

• Investigate two types of algorithms available in
  Oracle for data mining (ODM).
• Apply the two algorithms to actual data.
• Analyse
• Evaluate
• results in terms of performance.

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What is Data Mining? (Background)

• Simply put, DM is knowledge discovery.
• DM is the process of automatic discovery of
  hidden patterns and relationships within
  enormous amounts of data.
• It is a powerful new technology that allows
  businesses to make proactive, knowledge-driven
  decisions as it tries to predict the future.
• Data (represents knowledge) normally stored in
  databases and data warehouses ( typical size in
  tera-bytes).

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Automatic discovery is implemented by the use of
algorithms provided by DM suites

• E.g. oracle offers
• Adaptive Bayes Network supporting decision
  trees (classification)
• Naive Bayes (classification)
• Model Seeker (classification)
• k-Means (clustering)
• O-Cluster (clustering)
• Predictive variance (attribute importance)
• Apriori (association rules)

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• Algorithms are grouped as either supervised or
  unsupervised learning strategies.

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The data mining process involves a series of
steps to define a business problem, gather and
prepare the data, build and evaluate mining
models, and apply the models and disseminate the
new information.
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Expected Results

• Aim at conclusively saying which algorithm will
  be most effective and suitable for the process of
  data mining on any dataset - since datasets
  are different.

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Possible Extensions to the Project

• testing of the same algorithms with different
  tools offered by other vendors.
• e.g. testing with the DM suite in SQL and
  checking if the results are similar.
• If not, investigating why the results are
  different, could be another extension.

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Plan of Action

• Carry out a literature search
• mainly to obtain background knowledge and
  understanding of field.
• Get to know Oracle DM Suite
• Do DM tutorials provided by oracle.
• The server Ora1 is the machine Ill be working
  with.
• It is already installed with JDeveloper oracle
  10g database, oracle 9i DM.

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Timeline
Continuation from literature and tutorials done
Investigate Clustering Classification algorithms (theory) 2nd term- 15 to 30 April
Find suitable computerised case studies of the use of above algorithms with or without Oracle. 2nd term- End of May
Search databases for testing (possibilities AIDS data faculty data) 2nd term- End of May
Apply algorithms to data found then Critically Analyse assess results Second semester
Write up paper September vacation and 3rd term
Final project write up Due 7/11
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Literature Survey

• Richard J. Roiger and Michael W. Geatz, Data
  mining a tutorial- based primer. Boston,
  Massachusetts, Addison Wesley, 2003
• This book will provide the necessary background
  and practical knowledge required for the project
  research and also presents different
  methodologies used in data mining that may be
  useful.

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• David Hand, Heikki Mannila and Padhraic Smyth,
  Principles of data mining.
• Cambridge Massachusetts, MIT Press, 2001.
• Jesus Mena, Data mining your website. Digital
  Press, 1999.
• Jiawei Han and Micheline Kamber, Data mining
  concepts and techniques
• San Francisco, California, Morgan Kauffmann,
  2001
• Robert P. Trueblood and John N. Lovett, Jnr. Data
  Mining and Statistical Analysis Using SQL, USA,
  Apress,
• http//www.lc.leidenuniv.nl/awcourse/oracle/datami
  ne.920/a95961/preface.htm
• http//www.oracle.com/technology/products/oracle9i
  /htdocs/o9idm_faq.html
• http//fas.sfu.ca/cs/research/groups/DB/sections/p
  ublication/kdd/kdd.html .

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Questions? Thank you