2 A Tour of the Data Mining APIs

CREATE_MODEL

The CREATE_MODEL procedure creates a mining model. The attributes, transformations, rules, and other information internal to the model are returned by GET_MODEL_DETAILS functions. You can also obtain information about mining models by querying data dictionary views, as described in "Data Mining Data Dictionary Views".

APPLY

The APPLY procedure creates a table with specific columns and populates the columns with mining results. The columns of this table vary based on the particular mining function and algorithm.

Physical Data Set

Physical data sets refer to the data to be used as input to data mining operations. PhysicalDataSet objects reference specific data identified by a URI. Oracle Data Mining supports a table or view in the same database as a valid physical dataset URI. Syntax of the physical dataset URI is as follows:

Data URI Syntax:

[schemaName.] tableName/viewName

The PhysicalDataSet object can support multiple data representations. Oracle Data Mining supports two types of data representation: single-record case and wide data. (See Chapter 3 for details.) The Oracle Data Mining implementation requires users to specify the case ID column in the physical dataset.

A PhysicalDataSet object is a transient object in the Oracle Data Mining Java API. It is stored in the Connection as an in-memory object.

Build Settings

A BuildSettings object captures the high-level specification input for building a model. The API specifies mining functions: classification, regression, attribute importance, association, clustering, and feature extraction.

Build settings allow a user to specify the type of result desired without having to specify a particular algorithm. Although a build settings object allows for the specification of an algorithm and its settings, if the algorithm settings are omitted, the DME selects an algorithm based on the build settings and possibly characteristics of the data.

Build settings may also be validated for correct parameters using the verify method.

A BuildSettings object is persistent; it is stored as a table with a user-specified name in the user's schema. This settings table is interoperable with the PL/SQL API. Oracle recommends that you not modify the build settings table manually.

Task

The Execute method in the Connection object is used to start an execution of a mining task. Typically, mining operations are done using tables with millions of records, so the execution of operations such as a model build can take a long time.

JDM supports asynchronous execution of mining tasks using DBMS_SCHEDULER in the database. Each mining task is stored as a DBMS_SCHEDULER job object in the user's schema. When the user saves the task object, it creates a job object and sets the object to be in the DISABLED state. When the user executes a task, it enables the job to start execution.

To monitor tasks that are executed asynchronously, the Execute method returns a javax.datamining.ExecutionHandle object. It provides methods such as waitForCompletion and getStatus to retrieve the status details of the task.

Model

A Model object is the result of applying an algorithm to data as specified in a BuildSettings object.

Models can be used in several operations. They can be:

• Inspected, for example to examine the rules produced from a decision tree or association

• Tested for accuracy

• Applied to data for scoring

• Exported to an external representation such as native format or PMML

• Imported for use in the DME

When a model is applied to data, it is submitted to the DME for interpretation. A Model object references its BuildSettings object as well as the Task that created it. as.

Test Metrics

A TestMetrics object is the result of testing a supervised model with test data. Based on the type of mining function, different test metrics are computed. For classification models, accuracy, confusion-matrix, lift, and receiver-operating characteristic can be computed to access the model. Similarly for regression models, R-squared and RMS errors can be computed.

Apply Settings

An ApplySettings object allows users to tailor the results of an apply task. It contains a set of ordered items. Output can consist of:

• Data (key attributes) to be passed through to the output from the input dataset.

• Values computed from the apply itself: score, probability, and in the case of decision trees, rule identifiers.

• Multi-class categories for its associated probabilities. For example, in a classification model with target favoriteColor, users could select the specific colors to receive the probability that a given color is favorite.

Each mining function class defines a method to construct a default apply settings object. This simplifies the programmer's effort if only standard output is desired. For example, typical output for a classification apply would include the top prediction and its probability.

Transformation Sequence

A TransformationSequence object represents the sequence of transformations that are to be performed as part of a mining operation. For example, a Support Vector Machine model build involves outlier handling and normalization transformations. In addition to this, there can be new derived attribute creation and business transformations, and so on. Typically these transformations are reused for other model builds and hence applications can save the transformation sequence as a named object in the API.

Transformation sequences can be used either to perform transformations as a separate task or embed them to the modeling process by specifying it as one of the input objects for model building.