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Using the Stratified Sample Transformation Wizard

Data warehouse tips by Burleson Consulting

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

The stratified sample transformation is useful when your target attribute constitutes a small percentage of cases.  This transformation was utilized in Chapter 4 to create a dataset for clustering.  In that example we used the COIL dataset and increased the percentage of the target CARAVAN from 6% to 33% of the sampled data. 

We kept all attribute?s where CARAVAN = 1 (348 cases) and randomly selected cases where CARAVAN = 0 which made up 2/3 of the total sample.  An alternate method is using percentage of cases in Step 3 of the stratified sample wizard.  Click Percent of Cases, leaving 100% as the default.  In step 4 of 4, click the Equal Distribution button to create a case dataset with a 50% distribution of the target attribute.  The total sample count is 696 cases, which is 12% of the original COIL dataset. 

The SQL used to create this transformation is shown below:






( "CARAVAN"='1' and ORA_HASH(RNUM,(348-1),12345) < 348 )

 OR ( "CARAVAN"='0' and ORA_HASH(RNUM,(5474-1),12345) < 348 )

Using the Filter Single-Record Transformation Wizard

In some analysis, you may want to examine a subset of the case dataset.  You may see some interesting results in a cohort of diabetic patients, or want to explore more about customers in a specific region of the country.  The Filter Single Record transformation creates a view for the filtered dataset.  Filter conditions are specified using a Where clause, which is easy to construct using the expression builder. 

The resulting SQL to create the view is shown below:

CREATE VIEW "DMUSER_BOOK"."boston_price261754772" AS

SELECT * FROM "DMUSER_BOOK"."boston_price"

WHERE "boston_price"."CRIM"  < 8.0  And "boston_price"."NOX "  > 5.5

Inside the Sample Transformation Wizard

The Sample Transformation wizard creates a random sample of a table or view; the generated random sample can be either a table or a view. You can specify the sample size either as a number of records or as a percentage of records. You can also specify a random seed. A random sample is created by choosing a sample of cases picked at random.

The random sample is smaller than the set that it is based on. Using the smaller sample can result in improved performance in building a model or performing unsupervised data mining activities. If the sample is representative of the whole set, results generated using the sample will be compatible with the results generated using the whole set.

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:


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