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Oracle Data Mining

Data warehouse tips by Burleson Consulting

This is an excerpt from Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse".

What is Data Mining?

You quickly realize that pivot table analyses, while interesting, will take weeks or months of examination and since time is of the essence, you decide to try your hand at data mining.  Why?  Data mining is great at finding patterns in huge amounts data.  The Gartner Group the information technology research firm defines data mining (from their web site, Jan. 2004):

?Data mining is the process of discovering meaningful new correlations,
patterns and trends by sifting through large amounts of data stored in
repositories, using pattern recognition technologies as well as statistical
and mathematical techniques."

This is a wonderful but somewhat obtuse definition of data mining!  How do we start?  Let?s explore a little bit about the Oracle data warehousing tools. 

Oracle provides a powerful data mining infrastructure embedded directly into the database.  The data mining infrastructure, accessed through Java API, automates the performance of all the phases of data.  Even though data mining is based on statistics and machine learning  (i.e. artificial intelligence), you don?t have to have to be a statistical genius to run your data mining analysis with Oracle.

The approach to Oracle data mining follows these straightforward steps:

1.      Sample from a larger database or data warehouse.

2.      Explore, clean, preprocess and reduce the data, including treatment of outliers and       missing data.

3.      Develop an understanding of variables and selection of variables for building a model.

4.      Data is partitioned into training, validation and test data sets.

5.      Run several modeling techniques, choosing one on the basis of its performance on the  validation data. Results with the test data are an indicator of how well it will do with new  data. 

These steps will be explained in greater detail as we go along.

First of all, how will you obtain the customer data?  Do you have to decide what fields are important at the outset?  Fortunately, one of the strengths of data mining is that there are algorithms available to help you capture the important fields that are needed to build successful models of ?good? customers.  So don?t worry about deciding which fields you need, include as many as you can reasonably load into the table and let Oracle data miner help mine the gold from the data.

Components of Oracle Data Miner

To help you with steps one and two above, Oracle data miner has an impressive array of tools for sampling, exploring, and cleaning the data. These tools include importingfiles, recodingexisting fields, filtering using where queries, and deriving new fields. 

In addition, there are utilities for creating views, creating tables from views, copying tables, joining tables together, and importingtext files.  Displaying summary statistics and histograms assists in step three, developing an understanding of the data.

In order to build our model of the best customer to contact for our marketing blitz, we?ll need to partition the data into build and test data sets.  ODMrdoes this automatically for you, and we?ll explore these features in a future chapter.  Finally, we?ll develop a model, test it and then apply it to new data to obtain our customer mailing list.


 
For more tips and tricks for Oracle data warehouse analysis, see Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse"

You can buy it direct from the publisher for 30%-off:

http://www.rampant-books.com/book_2006_1_oracle_data_mining.htm


 

 

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Note: This Oracle documentation was created as a support and Oracle training reference for use by our DBA performance tuning consulting professionals.  Feel free to ask questions on our Oracle forum.

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