In this video Jarlath Quinn takes a first look at SPSS Modeler v18.2 and demonstrates some of the new functionality that’s included within this release.
IBM® SPSS® Modeler adds the following features in this release.
- New look and feel. A new modern interface theme is available via . For instructions on switching to the new theme.
- New data views. You can now right-click a data node and select View Data to examine and refine your data in new ways with advanced data visualizations.
- IBM Data Warehouse. Database modeling with IBM Netezza Analytics now supports IBM Data Warehouse.
- Gaussian Mixture node. A new Gaussian Mixture node is available on the Python tab and the Modeling tab of the Nodes palette.
- Kernel Density Estimation (KDE) nodes. A new KDE Modeling node is available on the Python tab and the Modeling tab of the Nodes palette.
- Hierarchical Density-Based Spatial Clustering (HDBSCAN) node. A new HDBSCAN node is available on the Python tab and the Modeling tab of the Nodes palette.
- JSON nodes. New JSON nodes are available for importing and exporting data in JSON format.
- AIX. AIX is a supported platform for 18.2.
- IBM SPSS Modeler Text Analytics enhancements. The following enhancements have been made. Most of these enhancements are similar to functionality found in IBM SPSS Text Analytics for Surveys .
- You can now import SPSS Text Analytics for Surveys projects (.tas) in the same way you can import resources from text analysis packages (.tap). When configuring a text mining modeling node, you must specify the resources that will be used during extraction. Instead of choosing a resource template, you can select a .tap or a .tas (new) in order to copy not only its resources but also a category set into the node.
- Flags are now available in the Data pane. You can flag documents with a “complete” flag or an “important” flag. A new column shows any flags you may be using, and you can click inside the column to change the flag type. This is useful for reviewing the completeness of a category model.
- Extracted concept results have been improved (they’re now similar to extracted concept results in SPSS Text Analytics for Surveys )
- Empty records are now handled the same was as they are in SPSS Text Analytics for Surveys . For example, with an Excel source file, empty records are now kept as part of the text.
- New Force In and Force Out options are available in the Data pane to force records into or out of a category. This is useful in the case of empty records or records with no extracted concepts, and also when no concept or TLA output enables you to find the appropriate category.
- Type Reassignment Rules (TRRs) are now available. TRRs transform a sequence of types, macros, and/or tokens into a new concept with a specific type. They can be used in Opinions templates to catch opinions with a change in polarity.
- A new option called Score only lowest-level matching category is available for generated text nuggets. Use this option to output a category only on one single line (for example, if the category is GeneralSatisfaction/Pos, selecting this option results in GeneralSatisfaction/Pos. Without this option, you would get two lines:GeneralSatisfaction and GeneralSatisfaction/Pos).
For more information about all these features see IBM’s Knowledge Center article on this topic.