Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics. It provides means for the creation, management and operational deployment of data mining models inside the database environment. Overview Oracle Corporation has implemented a variety of data mining algorithms inside its Oracle Database relational database product. These implementations integrate directly with the Oracle database kernel and operate natively on data stored in the relational database tables. This eliminates the need for extraction or transfer of data into standalone mining/analytic servers. The relational database platform is leveraged to securely manage models and to efficiently execute SQL queries on large volumes of data. The system is organized around a few generic operations providing a general unified interface for data-mining functions. These operations include functions to create, apply, test, and manipulate data-mining models. Models are created and stored as database objects, and their management is done within the database - similar to tables, views, indexes and other database objects. In data mining, the process of using a model to derive predictions or descriptions of behavior that is yet to occur is called "scoring". In traditional analytic workbenches, a model built in the analytic engine has to be deployed in a mission-critical system to score new data, or the data is moved from relational tables into the analytical workbench - most workbenches offer proprietary scoring interfaces. ODM simplifies model deployment by offering Oracle SQL functions to score data stored right in the database. This way, the user/application-developer can leverage the full power of Oracle SQL - in terms of the ability to pipeline and manipulate the results over several levels, and in terms of parallelizing and partitioning data access for performance. Models can be created and managed by one of several means. Oracle Data Miner provides a graphical user interface that steps the user through the process of creating, testing, and applying models (e.g. along the lines of the CRISP-DM methodology). Application- and tools-developers can embed predictive and descriptive mining capabilities using PL/SQL or Java APIs. Business analysts can quickly experiment with, or demonstrate the power of, predictive analytics using Oracle Spreadsheet Add-In for Predictive Analytics, a dedicated Microsoft Excel adaptor interface. ODM offers a choice of well-known machine learning approaches such as Decision Trees, Naive Bayes, Support vector