SPSS Modeler

Three editions for advanced predictive data mining

There are three editions of our SPSS Modeler predictive analytics workbench. This table compares the features of SPSS Modeler Professional and SPSS Modeler Premium. The third edition, SPSS Modeler Server, provides enterprise-level data access and deployment capabilities and additional features for enterprise users. SPSS Modeler Server is available in both Professional and Premium editions and requires the relevant client.

Note: Japanese Language Extractor licenses require SPSS Modeler Premium.

Key Features	Modeler Professional	Modeler Premium
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Create a wide range of interactive graphs with automatic assistance
Use visual link analysis to see the associations in your data
Interact with data by selecting regions or items on a graph and view the selected information; or select key data for use in analysis
Access IBM SPSS Statistics tools directly from interface
Read from and write to operational data from a variety of operational datasources such as IBM DB2®, Oracle®, Microsoft SQL Server™, Informix®, Neoview, Netezza, mySQL (Sun) and Teradata
Read from and write to data views and metadata stored in Cognos® 8 Business Intelligence
Read from and write to data views and metadata stored in Cognos
Import from and export to delimited and fixed-width text files, any IBM SPSS Statistics file, SAS, IBM Data Collection data sources, or XML
Data-cleaning options that remove or replace invalid data, automatically impute missing values and mitigate for outliers and extremes
Apply automatic data preparation to interrogate and condition data for analysis in a single step
Field filtering, naming, derivation, binning, re-categorization, value replacement, and field reordering
Record selection, sampling (including clustered and stratified sampling), merging (including inner joins, full outer joins, partial outer joins, and anti-joins), and concatenation; sorting, aggregation, and balancing
Data restructuring, partitioning and transposition
Extensive string functions: string creation, substitution, search and matching, whitespace removal, and truncation
RFM scoring: Aggregate customer transactions to provide recency, frequency, and monetary value scores and combine these to produce a complete RFM analysis
Use interactive model and equation browsers and view advanced statistical output
Show relative impact of different data attributes on predicted outcomes with variable importance graphs
Combine multiple models or use one model to analyze a second model
Use automatic classification (both binary and numeric) and clustering models to eliminate need for selecting individual algorithm
Use Modeler’s Component-Level Extension Framework (CLEF) to build custom algorithms
Through the integration of IBM SPSS Statistics, use R to extend analysis options through customization
C&RT, CHAID & QUEST: Decision tree algorithms including interactive tree building
Decision List: Interactive rule-building algorithm
K-Means, Kohonen, Two Step, Discriminant, Support Vector Machine (SVM): Clustering and segmentation algorithms
GRI: Generalized rule induction association discovery algorithm
Factor/PCA, Feature Selection: Data reduction algorithms
Regression, Linear, GenLin (GLM): Linear equation modeling
Self-learning response model (SLRM): Bayesian model with incremental learning
Time-series: Generate and automatically select time-series forecasting models
C5.0 decision tree and rule set algorithm
Neural Networks: Multi-layer perceptrons with back-propagation learning, and radial basis function networks
Support Vector Machines: Advanced algorithm with accurate performance for wide datasets
Bayesian Networks: Graphical probabilistic models
Cox regression: Calculate likely time to an event
Anomaly Detection: Detect unusual records through the use of a cluster-based algorithm
KNN: Nearest neighbor modeling and scoring algorithm
Apriori: Popular association discovery algorithm with advanced evaluation functions
CARMA: Association algorithm which supports multiple consequents
Sequence: Sequential association algorithm for order-sensitive analyses
Export models using SQL or PMML (the XML-based standard format for predictive models)
Extract text data from files, operational databases and RSS feeds (blogs, web feeds)
Select native language extractor options for Dutch, English, French, German, Italian, Portuguese, Spanish or Japanese or translate virtually any language using third-party software (separately licensed)
Extract domain-specific concepts such as uniterms, expressions, abbreviations, acronyms, and more
Calculate synonyms using sophisticated linguistic algorithms and embedded or user-specified linguistic resources
Name concepts by person, organization, term, product, location, and other user- defined types
Extract non-linguistic entities such as address, currency, time, phone number, and social security number (SSN)
Use and customize pre-built templates and libraries for sentiment analysis, CRM, security and intelligence, market intelligence, life sciences, and IT
Leverage pre-packaged Text Analytics Packages (TAPs) for the most common business applications and create your own
Create clusters based on term co-occurrence using concept clustering algorithms, which provide an at-a-glance view of main topics and the way in which they are related
Intelligently group text documents and records based on content, using text classification algorithms –Enable advanced concept selection and deselection for use in predictive modeling
Use text based and visual reports to interrogate concept relationship, occurrence, frequency and type
Identify and extract sentiments (for example, likes and dislikes) from text in Dutch, English, French, German, and Spanish
Ability to read from IBM Classic Federation data sources