Data Mining with Oracle using Classification and Clustering Algorithms Proposed and Presented by Nhamo Mdzingwa Supervisor: John Ebden

Presentation Outline  Problem Statement  Objective  Background  Expected Results  Possible Extensions  Plan of action  Timeline  Literature Survey  Questions

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 DB2’s Intelligent Miner Informix’s Data Mine SQL Data miner Ghost miner Clementine 9.0 (SPSS) SAS Gornish systems, etc

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.

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.

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).

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) 1. Model Seeker (classification) 2. k-Means (clustering) 3. O-Cluster (clustering) 4. Predictive variance (attribute importance) 5. Apriori (association rules)

Algorithms are grouped as either supervised or unsupervised learning strategies. DM strategies Unsupervised learning Supervised learning Classification Naive Bayes Model Seeker Adaptive Bayes Estimation Prediction Predictive variance Clustering k-Means O-Cluster Input attributes and output one or more attributes Input attributes but have no output attributes

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.

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.

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.

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 I’ll be working with. It is already installed with JDeveloper & oracle 10g database, oracle 9i DM.

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 paperSeptember vacation and 3rd term Final project write up Due 7/11 Timeline

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.

David Hand, Heikki Mannila and Padhraic Smyth, Principles of data mining. Cambridge Massachusetts, MIT Press, Jesus Mena, Data mining your website. Digital Press, 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, tm tm

Questions? Thank you