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One Click 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".

Clicking the Data Mining Activityin the Navigator pane shows a listing of activities that were created under each of the algorithms, and by selecting an activity the details of all the steps and the output, build, and test data created are shown for each step.  In previous releases, these tasks were not automated, leaving the data mining analyst to prep the data and create the interim data tables or views before building and testing each model.  The analyst kept notes of all the tables and views generated by this process in order to document the process and replicate the resultant datasets and analyses. 

These transformation wizards, although now automated, are still available in 10gR2 and allow analysts to customize the data mining tasks to their liking.  In this chapter we will also introduce predictive analytics, a fully automated ?one click? data mining algorithm included in ODMr.

ODMrActivity Builder Tasks

Let?s look at the Support Vector Mining Activitycreated for the Boston housing market. The model produced output data for the outlier treatment, missing values, normalization, split, build and test steps. 

The outlier treatment created an output dataset named (in the example case for this text, it is)

?DMUSER_BOOK?.?DM4J$VBOSTON_PRICE212110607?. 

This dataset can be viewed from the Data Viewer by clicking on the output data name.  You can view the structure of this dataset, scroll through the data and view the lineage to see where the data was generated from.  This dataset is then used as input for the next step in the Activity list, taking care of missing values, which results in a new output dataset named ?DMUSER _BOOK?.?DM4J$VBOSTON_PRICE569386744?, used as input to the splitting algorithm.   

Each step in the Activity generates an output table that is used in the following step.  This feature is a huge help to the analyst for keeping track of the many steps of building the final result set. 

For many business applications, the default settings used by the Activity Builder will be sufficient to guide data mining activities.  For normalizing and treating outlier data, we probably do not want to change the settings given by the data mining experts.  However, you may want to change the binning to something else that might have more meaning for the business rules you are interested in. 

In that case, you will want to use the Discretizewizard to prepare the data more to your liking.  There may also be situations where you choose to use a different normalizationscheme.  We?ll examine these transformation wizards in this chapter.  First, let?s review the Na?e BayesMining Activitywe created in Chapter One.  Recall that we created an Activity named ALL_US_NB1.  Selecting this Activity brings up the Data Mining Activity steps.   In the Mining Activity pane we can see that the case table is DMUSER_BOOK.MINING_DATA_BUILD_V_US, with the Unique Identifier CUST_ID, target attribute of AFFINITY_CARD, and comment ?Na?e Bayes classification for all US customers?.  

Note that ?Case Table? is a link that opens a Data Viewer window where you can see the Structure, Data, and View Lineage.  ODMr named the Mining Data ?DMUSER _BOOK?.?DM4J$VMINING_DATA_28226663?.  You can also link directly to this case table by clicking on MINING DATA underneath the comment.  

In the ?View Lineage? tab, we can see the SQL statement defining the case table for the Mining Activity:

SELECT

"MINING_DATA_BUILD_V_US"."CUST_ID" as "DMR$CASE_ID",  TO_CHAR( "MINING_DATA_BUILD_V_US"."AFFINITY_CARD") AS "AFFINITY_CARD",  "MINING_DATA_BUILD_V_US"."AGE" AS "AGE",  TO_CHAR( "MINING_DATA_BUILD_V_US"."BOOKKEEPING_APPLICATION") AS "BOOKKEEPING_APPLICATION",  TO_CHAR( "MINING_DATA_BUILD_V_US"."BULK_PACK_DISKETTES") AS "BULK_PACK_DISKETTES",  "MINING_DATA_BUILD_V_US"."CUST_GENDER" AS "CUST_GENDER",  "MINING_DATA_BUILD_V_US"."CUST_INCOME_LEVEL" AS "CUST_INCOME_LEVEL",  "MINING_DATA_BUILD_V_US"."CUST_MARITAL_STATUS" AS "CUST_MARITAL_STATUS",  "MINING_DATA_BUILD_V_US"."EDUCATION" AS "EDUCATION",  TO_CHAR( "MINING_DATA_BUILD_V_US"."FLAT_PANEL_MONITOR") AS "FLAT_PANEL_MONITOR",  TO_CHAR( "MINING_DATA_BUILD_V_US"."HOME_THEATER_PACKAGE") AS "HOME_THEATER_PACKAGE",  "MINING_DATA_BUILD_V_US"."HOUSEHOLD_SIZE" AS "HOUSEHOLD_SIZE",  "MINING_DATA_BUILD_V_US"."OCCUPATION" AS "OCCUPATION",  TO_CHAR( "MINING_DATA_BUILD_V_US"."OS_DOC_SET_KANJI") AS "OS_DOC_SET_KANJI",  "MINING_DATA_BUILD_V_US"."YRS_RESIDENCE" AS "YRS_RESIDENCE",  TO_CHAR( "MINING_DATA_BUILD_V_US"."Y_BOX_GAMES") AS "Y_BOX_GAMES"

FROM "DMUSER_BOOK"."MINING_DATA_BUILD_V_US"

The Sample Step was skipped in this example, so we go on to Discretize and click on the Output Data link to view the data.  Note that the AGE attribute has been binned so that ages have been coded as 1, 2 and 3. 

We can see what was done to bin the data by reviewing the SQL statement in the ?View Lineage? tab:

SELECT 

"AFFINITY_CARD",( CASE WHEN "AGE" < 32 THEN 1

WHEN "AGE" <= 44 THEN 2

WHEN "AGE" > 44 THEN 3

 end)  "AGE", "BOOKKEEPING_APPLICATION", "BULK_PACK_DISKETTES", "CUST_GENDER", "CUST_INCOME_LEVEL", "CUST_MARITAL_STATUS", "DMR$CASE_ID", "EDUCATION", "FLAT_PANEL_MONITOR", "HOME_THEATER_PACKAGE", "HOUSEHOLD_SIZE", "OCCUPATION", "OS_DOC_SET_KANJI",( CASE WHEN "YRS_RESIDENCE" < 3 THEN 1

WHEN "YRS_RESIDENCE" <= 5 THEN 2

WHEN "YRS_RESIDENCE" > 5 THEN 3

 end)  "YRS_RESIDENCE", "Y_BOX_GAMES"

FROM "DMUSER_BOOK"."DM4J$VMINING_DATA_28226663"

 

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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