|Rosella Machine Intelligence & Data Mining|
CMSR Data Miner / Machine Learning / Rule Engine Studio
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.)
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.)
- 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.)
- 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.
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.)
(Note that StarProbe data miner is replaced with CMSR Data Miner / Machine Learning / Rule Engine Studio.)