ClearVu Analytics Module features

Data mining software

Skillfully analyzing your data

All features of ClearVu Analytics (CVA) are described in the table below.

General features

Functionality/feature Description Basic Standard Professional
Graphical user interface Visualization, user-friendly workflow, integration of all modules, flexible configuration tools
Parallelism Parallel generation of models    
Simple installation Minimal requirements to the software environment and user  
Intuitive project management – persistency of projects

– project representation by exactly one filentation durch genau 1 Datei
Clipboard selection Copy/paste of figures and tables from ClearVu Analytics for use in other applications
Customer-specific configuration Adaptation to corporation design for graphical presentations and reports
Documentation – user-manual

– online-help

– training material (tutorial)
Data import Supports Excel-maps, CSV, and ods-format
Export of graphical presentations Raster images, SVG- and EMF-format
Modeling interface Interfacing with the statistical package “R” for using external modeling algorithms

Explore and configure

Functionality/feature Description Basic Standard Professional
Quality measures for variables  
Various visualizations Histograms, correlation matrix, scatterplots
Filter functions for variables  
Variable transformations Automatic suggestion of variable transformations for improving model quality
Grouping of variables Declarations of constraints applying to groups of variables (e.g., selection of two out of five variables)
Outlier detection Automatic outlier detection based on standard devations


Functionality/feature Description Basic Standard Professional
Automatic variable selection Hierarchical clustering for dimensionality reduction
Linear models  
Variable selection for linear models Forward- and backward selection
Rule-based fuzzy models Model representation as fuzzy rules, including rule inspection algorithms
Support vector machine (SVM)  
Decision trees  
Random forests  
Neural network  
Automatic meta-modeling Automatic generation and selection of the best model for the given data set
  Automatic adaptation of the model to the data
  Evaluation of a range of quality criteria for model quality
High dimensional model outputs Represented as a set of meta models
Measures for the importance of variables  
Sensitivity analysis of models Interactive 2-d and 3-d surface plots, ternary diagrams
  Con tour plots

Design of experiments

Functionality/feature Description Basic Standard Professional
Standard designs Factorial design  
  D-optimal design  
  Latin Hypersquare  
Space filling design Takes preexisting data points into account  
  EAllows for linear constraints  
  Takes variable group constraints into account  
Specific design for formulations Take filler substances into account  
Miscellaneous Editor for new variables  


Functionality/feature Description Basic Standard Professional
Integration with modeling Objective functions for optimization are based on models  
Objective function “pocket calculator” Objective functions can be defined as mathematical expression by using model outputserden  
Multiple objective functions Multiple objective optimization including graphical visualization of the Pareto-front  
Constraint editor General constraints on objective functions can be defined, including algebraic constraints  
Visualization of optimization Visualization of optimization progress and of the Pareto-front  
Powerful optimization algorithm Advanced evolutionary strategy for finding optimal solutions in high-dimensional multimodal search spaces  


Functionality/feature Description Basic Standard Professional
Sliderbar-analysis Exploration of models by means of interactive parameter variation  
  Easy to use, visual and fast evaluation of planned experiments  
  Visualization of the sensitivity of single parameters  

Excel Add-Ins

Functionality/feature Description Basic Standard Professional
Modeling Add-In Utilization of models within Excel as an Add-In (i.e., an Excel-cell calculates model based predictions based on ClearVu Analytics models)  
  Generation of models in Excel  
  Exchange of models between ClearVu Analytics and Excel  


Functionality/feature Description Basic Standard Professional
Command-Line features Utilization of all components (modeling, optimization) as command-line calls (without graphical user interface)    
  Design of experiments in batch mode    
  Modeling in batch mode    
  Optimization in batch mode    

New Features ClearVuAnalytics 2.2

  New Features ClearVu Analytics 2.2
Modeling engine Improved optimization of linear models

Option to predefine term structures in linear models, to directly represent known relationships between variables, when desired

Parallelization of modeling for multicore-CPUs
Model comparison Added plots of residuals for comparing models
User Interface Replaced module overviews with more clearly arranged sheets

New object selector elements for easier and faster selections of variables, models, etc.

Improved handling of variables, constraints and groups in the "explore and configure" module.
Command-Line Tool ClearVu Global Optimizer now available through the command line tool for stepwise optimization connected to an external objective function (requires additional license)


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divis intelligent solutions GmbH
Joseph-von-Fraunhofer-Str. 20
44227 Dortmund
+49 231 97 00 341