Datamining is the process of digging down into your business data to discover hidden patterns and relationships. Datamining solves a common problem: the more data you have, the more difficult and time-consuming it is to analyze and draw meaning from the data effectively.
IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
Access all of IBMSPSS Modeler's predictive capabilities, as well as IBMSPSS Statistics' data transformation, hypothesis testing and reporting capabilities, from a single interface.
This book guides you through data mining processes using the IBM SPSS Modeler. Discover the new edition with an extensive case study, exercises, and more.
The workflow in SPSS Modeler is built around the Cross-Industry Standard Process for DataMining (CRISP-DM) methodology. This methodology embeds your work in SPSS Modeler in a larger project with several phases.
IBM®SPSS® Modeler is a set of datamining tools that enable you to quickly develop predictive models using business expertise and deploy them into business operations to improve decision making.
SPSS Statistics is a statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation.
IBM®SPSS® Modeler is a set of datamining tools that enable you to quickly develop predictive models using business expertise and deploy them into business operations to improve decision making.
The best way to learn about datamining in practice is to start with an example. A number of application examples are available in the IBM®SPSS® Modeler Applications Guide, which provides brief, targeted introductions to specific modeling methods and techniques.