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CMSR Data Miner / Machine Learning / Rule Engine Studio
Predictive Modeling by Machine Learning & Rule Engine & Data Mining

CMSR Data Miner / Machine Learning / Rule Engine Studio (previously StarProbe Data Miner) provides an integrated environment for machine learning based predictive modeling, expert system shell rule engine and big data data mining. CMSR is a perfect platform to develop advanced models using deep learning techniques for business data, combining predictive models and business rules. And deploy models on the web and Android devices.

Full predictive model development life cycle support: Data Analysis, Variable Relevancy Analysis (correlation and confusion-map analysis), (Rule-based) Data Transformation, Modeling, Model Integration, Model Validation & Fitness Testing, Database Scoring & Updates, Model Deployment on the web and Android and embedded applications.

CMSR provides easy-to-use Graphical User Interfaces (GUI) with intuitive powerful model and data visualization tools. You can develop advanced powerful models very easily (without typing cranky commands and API parameters, except rules).

Developing quality optimal neural network models requires more than API calls or web browser interface. Step-by-step continuous incremental training with multiple datasets. Network fine tuning. Network pruning and compression. Testing and validation. Database scoring. These require advanced interactive graphical visualization tools. All these can be done using CMSR GUI interfaces.

Very powerful graphical visualizations, advanced power options and smooth integration with database systems make it well-suited for advanced power users as well as beginners.

Ideal for large enterprises: Simple to deploy advanced sophisticated models on Rosella BI Web Server and on Android devices. Very fast! Rosella BI Server can process complex RME-EP deep learning model HTTP/JSON requests over 5,000 times per second on average dual core computers. (Note that performance can vary depending on neural network sizes.)

Designed for developing AI-based business solutions and embedded applications. Predictive models can generate (program function) source code in Python, PHP, Java/JSP/Android, C/C++/Objective-C, C#, VB, Excel/VBA, Swift, Ruby, JavaScript/Node.js. It is self contained program function code. Just paste into your programs and add function call statements. Perfect for developing Android and iOS predictive applications. CMSR technology is well suited for outsourcing companies!

Excellent algorithms, advanced power tools, super fast, big data (up to 2 billion records), easy-to-use GUI interfaces, smooth database incorporation, superb graphics and user experience!

(1) Rule Engine for Machine Learning / Deep Learning / Expert Systems

  • RME-EP: Rule Engine with Machine Learning, Deep Learning, Neural Network
  • Easy to build "Deep Learning" hierarchically stacked multiple neural network models
  • Combines rule engine and machine learning for predictive modeling
  • Forward-chaining rule inference engine
  • Rules can be written to incorporate predictive models
  • Powerful easy-to-learn rule specification language (derived from SQL)
  • Easy-to-use Machine Learning Studio
  • CMSR Studio can generate bagging, deep learning, predictive model evaluation rules automatically
  • Can be used as rule-based data transformation tools
  • Implement Radial Basis Function (RBF) using SOM and Neural Network models
  • Support many regression, time-series and statistical functions
  • Randomization rules can be used to create sampling datasets from database tables
  • Database scoring: Apply RME-EP models to database records
  • Models are deployed on Web and Android
  • Want to develop super intelligent models? Start with the example in this page and expand it!

(2) Predictive Modeling by Machine Learning

  • Neural Network: ANN Multi-layer Deep Neural Network. Optimized for statistical predictive modeling. Support sigmoid (or logistic), hyperbolic tangent, softmax and softmax+(normalized) activation functions. (Continuous incremental learning with multiple training datasets, network pruning and compression, random data training, best model search, network evolution diagram, GPU JOCL/OpenCL computing support.)
  • SOM: Self Organizing Maps for Clustering. Used in scientific research.
  • Decision Tree (Cramer, Entrophy, GINI, Chisquare, Expected Accuracy, etc.) (Note that Cramer tends to produce trees with fewer nodes but more accurate than other well-known decision tree algorithms.)
  • Regression
  • Above predictive models can generate program (function) source code in Python, PHP, Java/JSP, C/C++/Objective-C, C#, VB and Excel/VBA, Swift, Ruby, JavaScript/Node.js.
  • In addition to source code integration, models are deployed on Web and Android
  • Rule-based predictive model integration
  • Correlation analysis for predictive modeling variable relevancy analysis
  • Database scoring: Apply predictive models to database records

(3) Clustering and Segmentation: Customer Prioritizing and Targeting

  • Neural Clustering (SOM) with Response and Profit Modeling/Analysis
  • Decision Tree with Response and Profit Modeling/Analysis
  • Database scoring: Apply segmentation models to database records
  • For step-by-step guides for customer segmentation, prioritizing and targeting, please read Customer Prioritizing and Segmentation (PDF).

(4) Hotspot Drill-down and Profiling

  • Hotspot Drill-down Analysis and Profiling

(5) Cross-sell Shopping Basket Analysis

  • Association Rules - Apriori Algorithm (Works with both database and file data. Big data!)
  • Co-items Shopping Basket Analysis

(6) Database Analytics for Big Data: Hive/Hadoop, MySQL/MariaDB, SQL Server, PostgreSQL, MS Access, ...

  • Power visual charts: 3D bars, bars, histograms, histobars, scatterplots, and more.
  • Time-series analysis for big data: group-by time-series trend analysis using regression, incorporating smoothing and seasonal adjustment. (In the following figure, green columns are time-series data. Orange columns are projected values from regression with seasonal adjustment.)
    Time-series analysis with seasonal adjustment for big data.
  • Crosstable and group-by reports.
  • Database metadata navigation and data browser.
  • Data import and export tools.
  • SQL query/batch tools.

For more, please read Big Data Analytics.

(7) Data Mining and Statistics

  • Cross tables with deviation/hotspot analysis.
  • Groupby tables with trend analysis.
  • Power visual charts: 3D bars, bars, histograms, Sankey diagram, histobars, scatterplots, boxplots, and more.

(8) Special Features

  • 64-bit integer and floating point data representation.
  • Large in-memory optimized for fast speed (currently up to 2GB).
  • Super fast and Big data (single dataset up to 2 billion records and 1 tera-bytes).
  • Connect to and update major relational DBMS through JDBC.
  • Data Miner optimized for MicroSoft MS SQL Server, MySQL / MariaDB, PostgreSQL, MS Office Access, Apache Hive + Hadoop.
  • Deploy/publish predictive models on Android phones and Android tablets with MyDataSay app. For downloads, click here.
  • Runs on multiple OS: Windows, Linux, Mac OS X / MacOS.

For 1 year free license downloads, please visit CMSR Download Request.
For free academic downloads, please visit CMSR Download Request.

MacOS Data Mining / Machine Learning Software

CMSR Data Miner / Machine Learning Studio is based on Java(TM). It runs on Mac OS X / macOS seamlessly.

[ Screenshots of CMSR Data Miner / Machine Learning Studio ]

(For full view, click the images.)

For 1 year free license downloads, please visit CMSR Download Request.
For free academic downloads, please visit CMSR Download Request.

(Note that StarProbe data miner is replaced with CMSR Data Miner / Machine Learning / Rule Engine Studio.)