Changes in This Release for Oracle Data Mining User's Guide

Changes in this release for Oracle Data Mining User’s Guide.

Oracle Data Mining User's Guide is New in This Release

  • This guide is new in release 12c. Oracle Data Mining User's Guide replaces two manuals that were provided in previous releases: Oracle Data Mining Administrator's Guide and Oracle Data Mining Application Developer's Guide.

  • Information about database administration for Oracle Data Mining is now consolidated in Administrative Tasks for Oracle Data Mining . The remaining chapters of this guide are devoted to application development.

  • Information about the Data Mining sample programs is now in The Data Mining Sample Programs.

Changes in Oracle Data Mining 12c Release 1 (12.1)

The following changes are documented in Oracle Data Mining User's Guide for 12c Release 1 (12.1).

New Features

The following features are new in this release:

  • Expanded prediction details

    The PREDICTION_DETAILS function now supports all predictive algorithms and returns more details about the predictors. New functions, CLUSTER_DETAILS and FEATURE_DETAILS, are introduced.

    See Prediction Details.

  • Dynamic scoring

    The Data Mining SQL functions now support an analytic clause for scoring data dynamically without a pre-defined model.

    See Dynamic Scoring.

  • Significant enhancements in text mining

    This enhancement greatly simplifies the data mining process (model build, deployment and scoring) when unstructured text data is present in the input.

    • Manual pre-processing of text data is no longer needed.

    • No text index must be created.

    • Additional data types are supported: CLOB, BLOB, BFILE.

    • Character data can be specified as either categorical values or text.

    See Mining Unstructured Text.

  • New clustering algorithm: Expectation Maximization

    See the following:

  • New feature extraction algorithm: Singular Value Decomposition with Principal Component Analysis

    See the following:

  • Generalized Linear Models are enhanced to support feature selection and creation.

    See The Data Mining Sample Programs.

Desupported Features

The following features are no longer supported by Oracle. See Oracle Database Upgrade Guide for a complete list of desupported features in this release.

  • Oracle Data Mining Java API

  • Adaptive Bayes Network (ABN) algorithm

Other Changes

The following are additional new features in this release:

  • A new SQL function, CLUSTER_DISTANCE, is introduced. CLUSTER_DISTANCE returns the raw distance between each row and the cluster centroid.

    See Scoring and Deployment .

  • New support for native double data types, BINARY_DOUBLE and BINARY_FLOAT, improves the performance of the SQL scoring functions.

    See Preparing the Data.

  • Decision Tree algorithm now supports nested data.

    See Preparing the Data.