Operation mode |
Asynchronous. |
Synchronous.
If asynchronous execution is required, use other Oracle database features like unified scheduler |
Algorithms |
ODM k-means algorithm. |
Different version of k-means algorithm; faster, handles sparse data, supports new distance metrics (cosine and fast cosine), handles categorical and numerical attributes, doesn't require binning (instead it normalizes numeric attributes)
O-Cluster not supported |
Model build specification |
Based in ODM classes: LAD (data location), PDS (format of input data), MFS (function settings), MAS (algorithm settings) |
Data location (schema) is passed in the argument list (default is user schema); mining function is passed in argument list; settings (function and algorithm) are passed in a single optional table |
Settings |
Provided through Java objects MAS (optional) and MFS. |
Provided through an optional settings table. |
Default settings |
Available for algorithms? |
Available for function and algorithm. |
Attribute form type |
LDS (explicit or convenience function) |
Automatically inferred from column data type; form types can be modified using views |
Location of input data and result tables |
LAD (Java object) |
Provided in the argument list as schema information; default is user schema |
Input data structure |
Supports both single-record case both single-record case (as a conventional relational table input) and multi-record case (as a table input in "transactional format") |
Supports both single-record case (as a conventional relational table input) and multi-record case (as a conventional relational table with nested table columns representing association of multiple attributes of the same kind with the same case identifier ("wide data") |
Model apply (data scoring) |
Flexible filtering specification |
Apply interface is provided; a separate interface to rank apply accepts a cost matrix input to enable results generation on the basis of cost |
Model evaluation |
Confusion matrix and lift metrics for classification, tightly coupled with models for maximum ease of use |
Provides a variety of evaluation metrics: confusion matrix, lift RMSE, and ROC. Not coupled with a model for maximum flexibility; allows use of different cost matrices at evaluation time and performance evaluation of non-ODM models |
Transformations (data preparation) |
Internal support for automatic binning and normalization. Other transformations must be performed as pre-processing. |
All transformation must be performed as pre-processing. Normalization and binning are supported by DBMS_DATA_MINING_TRANSFORM |
Model export and import |
PMML export/import for Naive Bayes and Association models; no support for native format |
Export and import of all models in native format; no support for PMML. |
Model comparison (finding the best model) |
Model Seeker builds multiple NB and ABN models and selects the "best" one |
Not supported |
Cross validation |
Automatic for NB models |
Not supported |