IBM SPSS Modeler 14.2 | 4.13 GB
IBM SPSS Modeler is a set of data mining tools that enable you to quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the industry-standard CRISP-DM model, SPSS Modeler supports the entire data mining process, from data to better business results. SPSS Modeler offers a variety of modeling methods taken from machine learning, artificial intelligence, and statistics. The methods available on the Modeling palette allow you to derive new information from your data and to develop predictive models. Each method has certain strengths and is best suited for particular types of problems.
SPSS Modeler Server runs continually in distributed analysis mode together with one or more IBM SPSS Modeler installations, providing superior performance on large data sets because memory-intensive operations can be done on the server without downloading data to the client computer. SPSS Modeler Server also provides support for SQL optimization and in-database modeling capabilities, delivering further benefits in performance and automation. At least one SPSS Modeler installation must be present to run an analysis.
More info: thisAbout SPSS, an IBM Company
SPSS, an IBM Company, is a leading global provider of predictive analytics software and solutions. The Company’s complete portfolio of products – data collection, statistics, modeling and deployment – captures people’s attitudes and opinions, predicts outcomes of future customer interactions, and then acts on these insights by embedding analytics into business processes. SPSS solutions address interconnected business objectives across an entire organization by focusing on the convergence of analytics, IT architecture and business process. Commercial, government and academic customers worldwide rely on SPSS technology as a competitive advantage in attracting, retaining and growing customers, while reducing fraud and mitigating risk. SPSS was acquired by IBM in October 2009.
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