Viscovery help customers uncover high value insights
Viscovery has released the latest Viscovery software suite designed to help customers uncover high-value insights in complex data sets. With a new modular architecture, Viscovery SOMine 7 provides improved analytic features and better support for an increasingly diverse user base. More intuitive workflows, better handling of Big Data, and numerous usability enhancements round out the latest Viscovery release.
The new data mining suite Viscovery SOMine 7 contains the complete feature set of the previous standalone tools of the Viscovery 6 product family. All functions formerly provided by these separate products – Viscovery Profiler, Viscovery Predictor, Viscovery Scheduler, and Viscovery SOMine – are now accessible through workflows of Viscovery SOMine 7. Viscovery SOMine 7 consists of a basic module, Visual Clusters, which can be operated as a standalone tool, and several extension modules, which allow the system to be flexibly expanded as needed. Viscovery SOMine projects now accommodate up to 5 workflows, depending on the licensed modules. Each workflow can be automated using the corresponding Workflow Automation Service, which is available as part of an additional package.
“Our clients are becoming increasingly diverse – ranging from marketing specialists, data scientists and consultants to researchers and clinicians,” says Dr. Gerhard Kranner, CEO of Viscovery Software. “This release has benefited tremendously from the novel applications and increasingly complex needs our users have presented. Viscovery SOMine 7 is a proven data-mining solution, and as we reach more users we strive to keep it always cutting edge, flexible, easy to use and even elegant.”
Viscovery SOMine version 7.0 is a full major release. Each modular configuration is available as a perpetual license or as a term-license for a specified period of time. Network licenses allow operation of the software for concurrent users.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.