IBM SPSS Modeler is a data mining software application from IBM. It is a data mining and text analytics workbench used to build predictive models. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming. SPSS Modeler has been used in these and other industries:
SPSS Modeler was originally named SPSS Clementine by SPSS Inc., after which it was renamed PASW Modeler in 2009 by SPSS.[8] It was since acquired by IBM in its 2009 acquisition of SPSS Inc. and was subsequently renamed IBM SPSS Modeler, its current name.
Editions
IBM sells the current version of SPSS Modeler (version 15) in two separate bundles of features. These two bundles are called "editions" by IBM:
- SPSS Modeler Professional: used for structured data, such as databases, mainframe data systems, flat files or BI systems
- SPSS Modeler Premium: Includes all the features of Modeler Professional, with the addition of:
- – Text analytics
- – Entity analytics
- – Social network analysis
Both editions are available in desktop and server configurations.
Architecture
SPSS Modeler has a three-tier design. Users manipulate icons and options in the front-end application on Windows operating systems. This front-end client application then communicates with the Modeler Server, or directly with a database or dataset. The most common configuration in large corporations is to house the Modeler Server software on a powerful analytical server box (Windows, UNIX, Linux), which then connects to the corporate Data warehouse. Data processing commands are automatically converted from the icon-based user interface into a command code (which is not visible) and is sent to the Modeler Server for processing. Where possible, this command code will be further compiled into SQL and processed in the data warehouse. NB -This section needs further updating
Features
Modeling Algorithms included
- Automatic classification (binary and numeric)
- Automatic clustering
- Anomaly detection
- Apriori
- Bayesian networks
- C&RT, C5.0, CHAID & QUEST
- CARMA
- Cox regression
- Decision List
- Factor analysis/PCA, feature selection
- K-means, Kohonen, two step, discriminant, support vector machine (SVM)
- KNN
- Logistic regression: For binary outcomes
- Neural networks: Multi-layer perceptrons with back-propagation learning, and radial basis function networks
- Regression, Linear, GenLin (GLM), generalized linear mixed models (GLMM): linear equation modeling
- Self-learning response model (SLRM)
- Sequence
- Support vector machine
- Time-series
Release history
- Clementine 1.0 – June 1994 by ISL[9]
- Clementine 5.1 – Jan 2000
- Clementine 12.0 – Jan 2008
- PASW Modeler 13 (formerly Clementine) – April 2009
- IBM SPSS Modeler 14.0 – 2010
- IBM SPSS Modeler 14.2 – 2011
- IBM SPSS Modeler 15.0 – June 2012
Product history
Early versions of the software were called Clementine and were Unix based and designed as a consulting tool and not for sale to customers. Originally developed by a UK company named Integral Solutions Limited (ISL),[9] the tool quickly garnered the attention of the data mining community (at that time in its infancy). Original in many respects, it was the first data mining tool to use an icon based Graphical user interface rather than requiring users to write in a Programming language.
In 1998 ISL was acquired by SPSS Inc., who saw the potential for extended development as a commercial data mining tool. In early 2000 the software was developed into a client / server architecture, and shortly afterward the client front-end interface component was completely re-written and replaced with a superior Java front-end.
SPSS Clementine version 12.0
The client front-end runs under Windows. The server back-end Unix variants (Sun, HP-UX, AIX), Linux, and Windows. The graphical user interface is written in Java.
IBM SPSS Modeler 14.2 was the first release of Modeler by IBM
IBM SPSS Modeler 15, released in June 2012, introduced significant new functionality for Social Network Analysis and Entity Analytics.
Competitors
See also
References
- ^ Forrester Research, Inc. (2012); The Forrester Wave™: Customer Analytics Solutions, http://www.forrester.com/pimages/rws/ reprints/document/80281/oid/1-KRB1C8
- ^ http://www-01.ibm.com/software/succes s/cssdb.nsf/CS/KKMH-88U29V?OpenDocume nt&Site=default&cty=en_us
- ^ http://www-01.ibm.com/software/analyt ics/spss/12/patient-outcomes/
- ^ http://www-01.ibm.com/software/succes s/cssdb.nsf/cs/STRD-8LJJGH?OpenDocume nt&Site=spss&cty=en_us
- ^ http://public.dhe.ibm.com/common/ssi/ ecm/en/imw14303usen/IMW14303USEN.PDF
- ^ http://public.dhe.ibm.com/common/ssi/ ecm/en/ytw03085usen/YTW03085USEN.PDF
- ^ Delen, Dursun (2009); Predicting Movie Box-Office Receipts Using SPSS Clementine Data Mining Software, in Nisbet, Robert; Elder, John; & Miner, Gary (2009). Handbook of Statistical Analysis and Data Mining Applications. Elsevier. pp. 391–415. ISBN 978-0-12-374765-5.
- ^ Oh My Darling! SPSS Says Goodbye Clementine, Hello 'PASW' – Intelligent Enterprise
- ^ a b Colin Shearer (1994); Mining the data-lode, Times Higher Education, November 18, 1994.
Further reading
- Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., et al. (2000). CRISP-DM 1.0, Chicago, IL: SPSS.
- Nisbet, R., Elder, J., and Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications. Burlington, MA: Academic Press (Elsevier).
External links
