Thursday May 27, 2010

New R Interface to Oracle Data Mining Available for Download

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Monday Mar 08, 2010

OpenWorld 2010 Call for Presentations is Now Open

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Thursday Feb 18, 2010

Oracle Data Mining Races with America's Cup

Oracle Data Mining was used by the performance analysis team of the BMW/Oracle Racing team in their preparation to win the America's Cup race off the coast of Spain.
BMW Oracle America Cup logo.jpg

The America's Cup has been away from U.S. shores for 15 years, the longest drought since 1851.  With the challenge of squeezing out every micro-joule of energy from the wind and with the goal of maximizing "velocity made good", the BMW Oracle Racing Team turned to Oracle Data Mining. 

"Imagine standing under an avalanche of data - 2500 variables, 10 times per second and a sailing team demanding answers to design and sailing variations immediately. This was the challenge facing the BMW ORACLE Racing Performance Analysis Team every sailing day as they refined and improved their giant 90 foot wide, 115 foot long trimaran sporting the largest hard-sail wing ever made. Using ORACLE DATA MINING accessing an ORACLE DATABASE and presenting results real time using ORACLE APPLICATION EXPRESS the performance team managed to provide the information required to optimise the giant multihull to the point that it not only beat the reigning America's Cup champions Alinghi in their giant Catamaran but resoundingly crushed them in a power display of high speed sailing. After two races - and two massive winning margins - the America's Cup was heading back to America - a triumph for the team, ORACLE and American technology."
--Ian Burns, Performance Director, BMW ORACLE Racing Team

America Cup Boat Blog pic.jpg

Visit the for pictures, videos and full information.

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Wednesday Feb 10, 2010

Funny YouTube video that features Oracle Data Mining

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Sunday Feb 07, 2010

Get Ready for the New Oracle Data Miner 11gR2 GUI!

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Friday Jan 29, 2010

Generating cluster names from a document clustering model

Text mining is a hot topic, especially for document clustering. Say you have a potentially large set of documents that you'd like to sort into some number of related groups. Sometimes it is enough to know which documents are in the same group (or cluster) and be able to assign new documents to the existing set of groups. However, you may also want a description of the clusters to help understand what types of documents are in those clusters. Automatically generating cluster names would be much easier than examining cluster centroids or reading a sample of documents in each cluster.

Oracle Data Mining supports this use case and below is a script that generates cluster names from a clustering model.

To use this script, you first need a clustering model and a text mapping table. These are easily produced using the Oracle Data Miner graphical user interface to automatically transform the data and then build the model. To get started, provide a data table with two columns: a numeric id column and a VARCHAR2 column containing the document text.

Here are a few key screen captures to guide you. I'm using a dataset from Oracle Open World that includes all the session text (title and abstract concatenated). By the way, this session document clustering was part of the process for producing the Session Recommendation Engine for Oracle Open World 2008 and 2009.

In Oracle Data Miner, start a build activity for clustering using k-Means. Then, select the dataset and the unique identifier, and click Next. (Click images to enlarge.)

ClusterNames 001.jpg

Check the SESSION_TEXT attributes as "input" and change the "mining type" to "text."

ClusterNames 002.jpg

Click advanced settings at the end of the wizard to reveal settings you can tailor. Since we have a single TEXT column, click on the tabs for "Outlier Treatment," "Missing Values," and "Normalize" and disable each step by clicking the box in the upper left-hand corner. Whereas these are often necessary for k-Means, our single text column and text transformation eliminate the need these.

Clicking the "Text" tab, you may specify various text-specific settings. For example, you may have a custom stopword list or lexer that you want to use, as shown below.

ClusterNames 003.jpg

Clicking the "Feature Extraction" sub-tab allows you to specify maximum number of terms to represent each document and the maximum number of terms to represent all documents.

ClusterNames 004.jpg

Click the "Build" tab to specify the number of clusters (groups) you want to have. For text, we recommend the "cosine" distance function. Depending on your needs, you may want to specify the split criterion to "size" to have clusters of more equal size. For a better model, set maximum interactions to 20.

ClusterNames 005.jpg

Oracle Data Miner now generates an activity that performs the text transformation and model building.

ClusterNames 006.jpg

To obtain the model name from the Build step, copy the text next to "Model Name." To obtain the mapping table, click the "Output Data" link under the Text step. Click the "Mapping Data" link and copy the name of the table at the top of the window.

ClusterNames 007.jpg

Now, you're nearly ready to invoke the following script to generate the cluster names.

Create a table like CLUSTER_NAME_MAP below to store the results. Then, replace the model name used below ('SESSION09_PRE92765_CL') with your model name, and the mapping table name used below (DM4J$VSESSION09_710479489) with your mapping table name.

create table cluster_name_map (model_name   VARCHAR(40),

                               cluster_name VARCHAR2(1999),

                               cluster_id   NUMBER,

                               record_count NUMBER);


Run this script on your model and table. Look below to see some sample output from the Open World session data. (Note that some columns are included in the script below, even though not required, to highlight data available in the model.)


  CURSOR ClusterLeafIds IS

    --Obtain leaf clusters


    FROM (

      SELECT distinct clus.ID AS CLUSTER_ID,

             clus.RECORD_COUNT RECORD_COUNT,

             clus.DISPERSION DISPERSION,

             clus.PARENT PARENT_CLUSTER_ID,

             clus.TREE_LEVEL TREE_LEVEL,


                                      ELSE 'NO' END IS_LEAF

       FROM (SELECT *

             FROM TABLE(dbms_data_mining.get_model_details_km('SESSION09_PRE92765_CL'))) clus,

                table(clus.child) chl


    WHERE is_leaf='YES'

    ORDER BY cluster_id;


  FOR c IN ClusterLeafIds LOOP

    INSERT INTO cluster_name_map (model_name, cluster_name,

                                  cluster_id, record_count)

    SELECT 'SESSION09_PRE92765_CL' model_name, cluster_name,

            c.cluster_id cluster_id, c.record_count record_count

    FROM (

      SELECT id, term || '-' ||

           LEAD(term, 1) OVER (ORDER BY id) || '-' ||

           LEAD(term, 2) OVER (ORDER BY id) || '-' ||

           LEAD(term, 3) OVER (ORDER BY id) || '-' ||

           LEAD(term, 4) OVER (ORDER BY id) cluster_name

      FROM (  

       SELECT id, text term, centroid_mean

       FROM (

        SELECT rownum id, a.*

        FROM (

             SELECT cd.attribute_subname  term,

                    cd.mean               centroid_mean

             FROM (

               SELECT *

               FROM TABLE(dbms_data_mining.get_model_details_km('SESSION09_PRE92765_CL',  c.cluster_id, null, 1, 0, 0, null)) ) a,

               TABLE(a.centroid) cd

             order by cd.mean desc) a

        WHERE rownum < 6) x,

        DM4J$VSESSION09_710479489 y

      WHERE x.term=y.attribute_id

      ORDER BY centroid_mean



    WHERE id=1;




Each cluster name is the concatenation of the top 5 terms (words with the highest ranking centroid values) that represent the cluster. The the image below, the second column is the cluster id, and the third column is the count of documents assigned to that cluster.

ClusterNames 008.jpg

Cluster names can also be assigned to the model clusters directly in the model.

Assigning cluster names and the advanced SQL in the script will be covered in future blog posts.


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Thursday Jan 28, 2010

Readable rules from a Decision Tree model

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Monday Jan 18, 2010

Fraud and Anomaly Detection Made Simple

Here is a quick and simple application for fraud and anomaly detection.  To replicate this on your own computer, download and install the Oracle Database 11g Release 1 or 2.  (See for more information).  This small application uses the Automatic Data Preparation (ADP) feature that we added in Oracle Data Mining 11g.  Click here to download the CLAIMS data table.  [Download the .7z file and save it somwhere, unzip to a .csv file and then use SQL Developer data import wizard to import the claims.csv file into a table in the Oracle Database.]

First, we instantiate the ODM settings table to override the defaults.  The default value for Classification data mining function is to use our Naive Bayes algorithm, but since this is a different problem, looking for anomalous records amongst a larger data population, we want to change that to SUPPORT_VECTOR_MACHINES.  Also, as the 1-Class SVM does not rely on a Target field, we have to change that parameter to "null".  See for detailed Documentation on ODM's anomaly detection.

drop table CLAIMS_SET;

exec dbms_data_mining.drop_model('CLAIMSMODEL');

create table CLAIMS_SET (setting_name varchar2(30), setting_value varchar2(4000));


insert into CLAIMS_SET values ('PREP_AUTO','ON');


Then, we run the dbms_data_mining.create_model function and let the in-database Oracle Data Mining algorithm run through the data, find patterns and relationships within the CLAIMS data, and infer a CLAIMS data mining model from the data.  


dbms_data_mining.create_model('CLAIMSMODEL', 'CLASSIFICATION',




After that, we can use the CLAIMS data mining model to "score" all customer auto insurance policies, sort them by our prediction_probability and select the top 5 most unusual claims.  

-- Top 5 most suspicious fraud policy holder claims

select * from

(select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud,

rank() over (order by prob_fraud desc) rnk from

(select POLICYNUMBER, prediction_probability(CLAIMSMODEL, '0' using *) prob_fraud


where PASTNUMBEROFCLAIMS in ('2 to 4', 'more than 4')))

where rnk <= 5

order by percent_fraud desc;

Leave these results inside the database and you can create powerful dashboards using Oracle Business Intelligence EE (or any reporting or dashboard tool that can query the Oracle Database) that multiple ODM's probability of the record being anomalous times (x) the dollar amount of the claim, and then use stoplight color coding (red, orange, yellow) to flag only the more suspicious claims.  Very automated, very easy, and all inside the Oracle Database! <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-46756583-1']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + ''; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script>

Powerful, Yet Simple: In-Database SQL Data Mining Functions

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Thursday Jan 07, 2010

Welcome to Oracle Data Mining!

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