Supervised learning is also known as directed learning. The learning process is directed by a previously known dependent attribute or target. Directed data mining attempts to explain the behavior of the target as a function of a set of independent attributes or predictors.

Supervised learning generally results in predictive models. This is in contrast to unsupervised learning where the goal is pattern detection.

The building of a supervised model involves training, a process whereby the software analyzes many cases where the target value is already known. In the training process, the model "learns" the logic for making the prediction. For example, a model that seeks to identify the customers who are likely to respond to a promotion must be trained by analyzing the characteristics of many customers who are known to have responded or not responded to a promotion in the past.

Unsupervised learning is non-directed. There is no distinction between dependent and independent attributes. There is no previously-known result to guide the algorithm in building the model.

Unsupervised learning can be used for descriptive purposes. It can also be used to make predictions.

Oracle Data Mining supports the supervised data mining algorithms described in the following table. The algorithm abbreviations are used throughout this manual.

Learn about unsupervised algorithms that Oracle Data Mining supports.

Oracle Data Mining supports the unsupervised data mining algorithms described in the following table. The algorithm abbreviations are used throughout this manual.

Oracle Data Mining offers several features that significantly simplify the process of data preparation:

• Embedded data preparation: The transformations used in training the model are embedded in the model and automatically executed whenever the model is applied to new data. If you specify transformations for the model, you only have to specify them once.

• Automatic Data Preparation (ADP): Oracle Data Mining supports an automated data preparation mode. When ADP is active, Oracle Data Mining automatically performs the data transformations required by the algorithm. The transformation instructions are embedded in the model along with any user-specified transformation instructions.

• Automatic management of missing values and sparse data: Oracle Data Mining uses consistent methodology across mining algorithms to handle sparsity and missing values.

• Transparency: Oracle Data Mining provides model details, which are a view of the attributes that are internal to the model. This insight into the inner details of the model is possible because of reverse transformations, which map the transformed attribute values to a form that can be interpreted by a user. Where possible, attribute values are reversed to the original column values. Reverse transformations are also applied to the target of a supervised model, thus the results of scoring are in the same units as the units of the original target.

• Tools for custom data preparation: Oracle Data Mining provides many common transformation routines in the DBMS_DATA_MINING_TRANSFORM PL/SQL package. You can use these routines, or develop your own routines in SQL, or both. The SQL language is well suited for implementing transformations in the database. You can use custom transformation instructions along with ADP or instead of ADP.

Most data mining algorithms act on single-record case data, where the information for each case is stored in a separate row. The data attributes for the cases are stored in the columns.

When the data is organized in transactions, the data for one case (one transaction) is stored in many rows. An example of transactional data is market basket data. With the single exception of Association Rules, which can operate on native transactional data, Oracle Data Mining algorithms require single-record case organization.

Prepare and transform unstructured text data for data mining.

Oracle Data Mining interprets CLOB columns and long VARCHAR2 columns automatically as unstructured text. Additionally, you can specify columns of short VARCHAR2, CHAR, BLOB, and BFILE as unstructured text. Unstructured text includes data items such as web pages, document libraries, Power Point presentations, product specifications, emails, comment fields in reports, and call center notes.

Oracle Data Mining uses Oracle Text utilities and term weighting strategies to transform unstructured text for mining. In text transformation, text terms are extracted and given numeric values in a text index. The text transformation process is configurable for the model and for individual attributes. Once transformed, the text can by mined with a data mining algorithm.

All Oracle Data Mining scoring routines support parallel execution for scoring large data sets.

In-database scoring provides performance advantages. All Oracle Data Mining scoring routines support parallel execution, which significantly reduces the time required for executing complex queries and scoring large data sets.

In-database mining minimizes the IT effort needed to support data mining initiatives. Using standard database techniques, models can easily be refreshed (re-created) on more recent data and redeployed. The deployment is immediate since the scoring query remains the same; only the underlying model is replaced in the database.

In Oracle Data Mining, scoring is performed by SQL language functions. Understand the different ways involved in SQL function scoring.

The functions perform prediction, clustering, and feature extraction. The functions can be invoked in two different ways: By applying a mining model object (Example 3-1), or by executing an analytic clause that computes the mining analysis dynamically and applies it to the data (Example 3-2). Dynamic scoring, which eliminates the need for a model, can supplement, or even replace, the more traditional data mining methodology described in "The Data Mining Process".

In Example 3-1, the PREDICTION_PROBABILITY function applies the model svmc_sh_clas_sample, created in Example 2-1, to score the data in mining_data_apply_v. The function returns the ten customers in Italy who are most likely to use an affinity card.

In Example 3-2, the functions PREDICTION and PREDICTION_PROBABILITY use the analytic syntax (the OVER () clause) to dynamically score the data in mining_data_apply_v. The query returns the customers who currently do not have an affinity card with the probability that they are likely to use.

SELECT cust_id FROM
  (SELECT cust_id, 
        rank() over (order by PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1
                     USING *) DESC, cust_id) rnk
   FROM mining_data_apply_v
   WHERE country_name = 'Italy')
WHERE rnk <= 10
ORDER BY rnk;

   CUST_ID
----------
    101445
    100179
    100662
    100733
    100554
    100081
    100344
    100324
    100185
    101345

SELECT cust_id, pred_prob FROM
  (SELECT cust_id, affinity_card, 
    PREDICTION(FOR TO_CHAR(affinity_card) USING *) OVER () pred_card,
    PREDICTION_PROBABILITY(FOR TO_CHAR(affinity_card),1 USING *) OVER () pred_prob
   FROM mining_data_build_v)
WHERE affinity_card = 0
AND pred_card = 1
ORDER BY pred_prob DESC;

   CUST_ID PRED_PROB
---------- ---------
    102434       .96
    102365       .96
    102330       .96
    101733       .95
    102615       .94
    102686       .94
    102749       .93
    .
    .
    .
    101656       .51