machines, Generalized linear model (GLM) for predictive mining, Association rules, K-means and Orthogonal Partitioning Clustering, and Non-negative matrix factorization for descriptive mining. A minimum description length based technique to grade the relative importance of input mining attributes for a given problem is also provided. Most Oracle Data Mining functions also allow text mining by accepting text (unstructured data) attributes as input. Users do not need to configure text-mining options - the Database_options database option handles this behind the scenes. History Oracle Data Mining was first introduced in 2002 and its releases are named according to the corresponding Oracle database release: Oracle Data Mining 9iR2 (9.2.0.1.0 - May 2002) Oracle Data Mining 10gR1 (10.1.0.2.0 - February 2004) Oracle Data Mining 10gR2 (10.2.0.1.0 - July 2005) Oracle Data Mining 11gR1 (11.1 - September 2007) Oracle Data Mining 11gR2 (11.2 - September 2009) Oracle Data Mining is a logical successor of the Darwin data mining toolset developed by Thinking Machines Corporation in the mid-1990s and later distributed by Oracle after its acquisition of Thinking Machines in 1999. However, the product itself is a complete redesign and rewrite from ground-up - while Darwin was a classic GUI-based analytical workbench, ODM offers a data mining development/deployment platform integrated into the Oracle database, along with the Oracle Data Miner GUI. The Oracle Data Miner 11gR2 New Workflow GUI was previewed at Oracle Open World 2009. An updated Oracle Data Miner GUI was released in 2012. It is free, and is available as an extension to Oracle SQL Developer 3.1 . Functionality As of release 11gR1 Oracle Data Mining contains the following data mining functions: Data transformation and model analysis: Data sampling, binning, discretization, and other data transformations. Model exploration, evaluation and analysis. Feature selection (Attribute Importance). Minimum description length (MDL). Classification. Naive Bayes (NB). Generalized linear model (GLM) for Logistic regression. Support Vector Machine (SVM). Decision Trees (DT). Anomaly detection. One-class Support Vector Machine (SVM). Regression Support Vector Machine (SVM). Generalized linear model (GLM) for Multiple regression Clustering: Enhanced k-means (EKM). Orthogonal Partitioning Clustering (O-Cluster). Association rule learning: Itemsets and association rules (AM). Feature extraction. Non-negative matrix factorization (NMF). Text and spatial mining: Combined text and non-text columns of input data. Spatial/GIS data. Input sources and data preparation Most Oracle Data Mining functions accept as input one relational table or view. Flat data can be combined with transactional data through the use of nested columns, enabling