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Oracle Data Mining Classification Models

Data warehouse tips by Burleson Consulting

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

Oracle Data Miner gives us the choice of four different classification models, Na?e Bayeswhich was described in Chapter 1, Adaptive Bayes, Decision Tree and Support Vector Machine.  Each approach has distinct advantages over the other, so which one will be the best?  The exploratory nature of data mining lends itself to investigating many different techniques.  As you saw in the last chapter, the Na?e Bayes model can be ?tweaked? to perform better given the nature of the data or results you are interested in. 

As a general rule, try several different methods and examine the differences between the results.  Because we are looking for patterns that are most likely unknown to us, we may not even find any useful results at all!  The patterns we see may not be meaningful or practical to apply.  The usefulness of a method can depend on the size of the dataset, the types of patterns that may exist in the data, meeting the underlying assumptions of the algorithm, the type of data, the goal of the analysis, and many other factors.

Using the Models

In this chapter we will describe the Adaptive Bayes Networkand Decision Treemodels.  We will use the import tool to import data into the Oracle database, and describe how to configure the models to produce the best results for our dataset, using attribute importance, costs and priors. 

We start with an example of predicting actual forest cover type using geographical data from the US Forest Service Resource Information System data.  The dataset available on the UCI KDD Archives site has 581,012 observations with 54 attributes regarding geological survey characteristics of the land, wilderness area designation, and soil type.  The target classifications are 7 types of forest cover, including 1 = spruce/fir, 2 = lodgepole pine, 3 = ponderosa pine, 4 = cottonwood/willow, 5 = aspen, 6 = Douglas-fir, and 7 = Krummholz.

Importing Model Data

We start by importing the data using the ODMrImport Wizard found under the ?Data? tab.  The dataset is comma delimited, and since there are no column headings, you have the option of changing the column name (be sure to enclose the variable names with ?? e.g. ?TARGET?) and designating the data type and size.  You can preview the data.  Specify a new table name COVER_TYPE_IMP, then click ?Next? and ?Finish?. 

This dataset will be imported using SQL*Loader, and for the Import Wizard to work you must set the directory for the SQL*Loader under Tools, Preferences in the ?Environment? tab. 

We right click on the table name you just created, COVER_TYPE_IMP, choose ?Show Summary Single Record?, and check the data.  ODMrwill guess whether the data type is numerical or categorical; you may need to change the TARGET attribute (type of forest cover) from numerical to categorical.


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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