CMSR Data Miner Suite
CMSR Data Miner Suite provides an integrated environment for predictive modeling, segmentation, data visualization, statistical data analysis, and rule-based model evaluation. It also provides integrated analytics and rule-engine environment for advanced power users.
Predictive Analytics Software
CMSR Data Miner Suite provides an integrated environment for predictive modeling, segmentation, data visualization, statistical data analysis, and rule-based model evaluation. It also provides integrated analytics and rule-engine environment for advanced power users. Main features of CMSR include Self Organizing Maps (SOM) – Neural Clustering, Neural network predictive modeling, (Cramer) Decision tree classification and Segmentation, Hotspot drill-down and profiling analysis, Regression, Radial Basis Function (RBF) with rule engine and Business rules – Predictive Expert systems shell engines.RME-EP (Rule-based Model Evaluation – Event Processing) has been developed to use SQL-like language which can be learnt by business users and domain experts very easily and quickly. RME-EP is based on SQL-99 syntax supporting most logical and arithmetic language features. Predictive Modeling. The core concept of RME-EP is Rule-based Model Evaluation (RME). RME-EP incorporates predictive models with many logical and mathematical functions of SQL-99 as patterns. By incorporating advanced predictive models into Rete engine, RME-EP provides a superb platform for advanced rule-based expert systems. RME-EP supports the following predictive models; Neural network, Neural Clustering and Radial Basis Functions, Decision tree, Regression and Time-series analysis.
Neural network is a very powerful predictive modeling technique. CMSR/RME-EP provides neural modeling tools which are robust and intuitive but still easy to use. Neural network can be used to predict numerical scores as well as classifications. Neural clustering also known as Self Organizing Maps (SOM) is a very powerful clustering and segmentation method. When combined with other modeling methods, it renders very poweful Radial Basis Functions.
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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.