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Using the Numeric 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 Numeric Transformation wizard allows you to create a view by applying one of a list of predefined functions to one or more numeric attributes. These functions modify the data distribution characteristics and/or normalize the data values.  You can select one of the following predefined schemes:

EXP(x), where x <= 70 (Oracle database limit)

1 / EXP(AVG(x) - x), where AVG(x) - x <= 70 (Oracle database limit)

LN(x + a), where a is a user-supplied numeric constant and x + a > 0; LN is natural logarithm. Use this scheme when you are dealing with large numbers; this transformation makes the distribution more like a normal distribution.

LN((x - a) / (b - x)), where a and b are user-supplied numeric constants and (x - a) / (b - x) > 0; LN is the natural logarithm.

LOG(10, x + a), where a is a user-supplied numeric constant and x + a > 0; LOG(10, z) is the logarithm to the base 10 of z. Use this scheme when you are dealing with large numbers; this transformation makes the distribution more like a normal distribution.

SQRT(x), where x >= 0. Use this function to linearize a distribution.

Using the Outlier Treatment Transformation Wizard

The Outlier Treatment Transformation is used to generate recommended outlier treatments based on the mining algorithm that you plan to use. If you invoke this wizard from a Mining Activity, the appropriate default treatments are specified automatically.

The default algorithm settings are shown below.  An outlier is a data point that is located far from the rest of the data. An outlier is typically several standard deviations from the mean. Some data mining algorithms are sensitive to outliers in data.  The Outlier Treatment wizard identifies outliers and lets you specify how to treat them.

You specify a treatment by defining what constitutes an outlier (for example, all values in the top and bottom 5% of values) and how to replace outliers (either with NULL or edge values). The wizard can generate default treatments based on the algorithm that you are planning to use. 

If you like, you can specify how to identify outliers using the wizard.

The SQL statement shows the wizard-generated code when the attribute CMEDV in the Boston price dataset is set to Standard Deviation, Multiples of Sigma = 3.

CREATE VIEW "DMUSER_BOOK"."boston_price58837524"

 AS SELECT

"AGE","B","CHAS",( CASE WHEN "CMEDV" < -5.02 THEN NULL

WHEN "CMEDV" >= -5.02 AND "CMEDV" <= 50.08 THEN "CMEDV"

WHEN "CMEDV" > 50.08 THEN NULL

end)  "CMEDV"

,"CRIM","DIS","INDUS","LAT","LON","LSTAT","MEDV","NOX"
,"OBS","PTRATIO","RAD","RM","TAX","TOWN","TOWN#","TRACT","ZN" FROM "DMUSER
_BOOK"."boston_price"

 

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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