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Data Classification in ODM

Data warehouse tips by Burleson Consulting

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

To start the classification problem, we?ll begin by using the Naive Bayes data mining activity.  The Naive Bayes algorithm has the advantages of being quick to run.   

The Mining ActivityBuild wizardis launched from the Activity pull-down menu.  Select Build to activate the wizard and click Next on the Welcome page.  Choose the Classification function type, Na?e Bayes algorithm, and click Next.   

In step 2 of the New Activity Wizard, select MINING_DATA_BUILD_V _US as the case table or view, choose Single Key CUST_ID as the unique identifier, and select all columns to include in the analysis.  Note that clicking Sampling Settings opens a new window that allows you to change how the data is sampled.  For this exercise you?ll keep the default Random sampling. 

In classification problems, a target is identified and in this case we are using the attribute AFFINITY_CARDto distinguish high-value customers where 1 = High-value and 0 = Low-value.   On step 3 of the New Activity Wizard, choose AFFINITY_CARD as the Target column.  Note that COUNTRY_NAME and PRINTER_SUPPLIES are not selected as attribute variables.  Neither of these fields will contribute the classification model because there is only one country in this view and all consumers order printer supplies.  You can see this by clicking the Data Summary link and seeing that the average, max, and min columns for PRINTER_SUPPLIES is 1, with 0 variance. 

In step 4 of the New Activity Wizard, select 1 as the preferred target value which identifies the cases we are trying to target, where our best customers have AFFINITY_CARD = 1.

Naming Data Mining Activities

For step 5, ODMr provides a name for the data mining activity, but you?ll probably want to change this to a name that explains the activity, such as ALL_US_NB1 for all US customers, Na?e Bayes activity 1. 

On the final page, the New Activity Wizard is complete and the Data Mining Activity is set to run upon finish when you click the Finish button. 

Click Advanced Settings to display and possibly modify the default settings.  

The Advanced Settings Dialog window shows three tabs: Sample, Discretize, Split, Build and Test Metrics.  The wizard has determined that samplingis not needed for this small dataset, and you?ll want to leave the Enable Step box not checked. 

The details of the Na?e Bayes classification model will be discussed in more detail in a later Chapter.  For now, accept the defaults and finish the model.

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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