Insightful Miner Feature List

What's New in Insightful Miner 7

• New and improved data processing nodes including multi-input join and append; detect duplicates; and reorder columns
  
  
• Improved graphics tools such as a new trellis hexbin plot and hexbin matrix, and the ability to create charts without the need for sampling
  
  
• Extensions to expression language through improved string
  and date handling
  
  
• Extended file format support including support for 64-bit SAS® and compressed SAS and new report and graphics output formats
  
  
• Performance improvements including enhanced sort, shuffle, and join capabilities
  
  
• The S-PLUS Script Node and over 20 charting nodes are now included, no separate license of S-PLUS required
  
  
• Two versions available including a desktop version for individual users and the Insightful Miner Server for scalable data analysis throughout your organization

Insightful Miner Feature List

Visual Workflow Environment

• Create self-documenting visual programs
• Intuitive drag-and-drop interface
• Link nodes together to describe analytic process
• On-screen annotations
• Node-level change-tracking for multi-user collaboration
• Visual confirmation of validity and caching
• Save and share worksheets as templates for best practices
• Export worksheet image to a file

• Delimited ASCII files
• Fixed format ASCII
• Data dictionary support
• SAS®, SPSS®, Excel® & many other flat file formats
• ODBC access to compliant databases (Windows®)
• Native access Oracle®, DB2, Microsoft® SQL Server, Sybase

Data Manipulation

• Powerful sampling, including stratified methods
• Row: Aggregate, Append, Filter, Partition, Sample, Shuffle, Sort, Stack, and Unstack
• Column: Bin, Create, Filter, Join, Modify, Reorder, Transpose and Normalize
• Automatically bin continuous variables
• Continuous, date, categorical and string data types
• Create or modify columns and filter rows using powerful expression language

Data Cleaning

• Detect and repair missing values with variance-preserving methods
  
• Detect duplicates
  
• Missing value handling: drop, replace, impute and last observation carried forward
  
• Detect multi-dimensional outliers with leading-edge robust methods
  

Exploratory Data Analysis and Visualization

• Trellis graphics quickly show structure of high-dimension data
  
• Univariate descriptive statistics, plus Correlation and Covariance calculations
  
• Table views and Visual Crosstabs rapidly slice and dice data
  
• Compare datasets for validation purposes
  
• 1-D Charts: Pie, Bar, Column, Dot, Histogram, Boxplot
  
• 2-D Charts: Scatterplot, Boxplot, Strip plot, Quantile-Quantile, Density
  
• Hexagonal Binning chart to view relationships between variables of very large data sets
  
• 3-D Charts: Contour, Level plot, Surface plot, Cloud plot
  
• Multivariate charts: Multiple 2-D plot, Scatterplot matrix, Hexbin Matrix, Parallel plot
  
• Time series charts: Line plot, High-Low plot, Stacked Bar plot
  

Model Types, Algorithms and Visualizers

• Prediction and classification outcome models with basic and advanced model options
  
• Highly scalable algorithms: train models on very large data sets without the need for sampling or aggregation
  
• Decision trees for classification and regression with single-tree or ensemble techniques using Block Model Averaging™; K-Fold cross-validation, plus Gini and Entropy splitting rules
  
• Linear and logistic regression implemented as QR decomposition with Householder transformations
  
• Neural Networks with Multi-layer perceptrons
  
• Neural Network training methods: Resilient Propagation, Quick Propagation, Delta-Bar-Delta, Conjugate Gradient, and Online methods.
  
• Neural Networks: up to three hidden layers with user-specified number of nodes per layer
  
• Interactive Neural Network visualizer allows real-time control over learning process
  
• Naïve Bayes Classifier
  
• Principal components analysis
  
• Cox Proportional Hazard models for censored data with time-varying covariates
  
• Customer segmentation models with K-Means Clustering
  
• Collapsible tree viewer with interactive dendrogram
  
• Assess models with gain charts, lift charts, ROC charts and agreement matrices
  
• Variable importance tool for selection of the most significant variables
  
• Automatic calculation of dummy variable and interaction columns
  

Scalability

• All components operate out-of memory and in-memory
  
• Unique "Pipeline Architecture" moves data in blocks through processing components
  
• Classical incremental techniques
  
• Block Model Averaging™ techniques
  
• Tailor size of blocks to optimize use of computing resources
  
• Automatic and manual control of caching to balance quick response with massive scalability
  

Extensibility

• Compound nodes: create an entire process within a single node
  
• Create new nodes using S programming language
  
• Complete access to all S-PLUS 7 Enterprise Developer functions and libraries through S programming language
  
• Create custom predictive models, charts and reports
  
• Create and share user libraries of custom nodes
  
• Manage multiple custom libraries
  

Deployment and Scoring

• Web-ready graphical reports
  
• HTML. PDF, PostScript and RTF model summary exports
  
• Non-interactive batch execution of all components*
  
• Model ports support automatically-updating scoring components
  
• Score custom predictive models created using S-PLUS on very large databases
  
• Predictive Model Markup Language (PMML) model import and export
  
• Generate C code for run-time model scoring*
  
  Note: * Requires Insightful Miner Server
  

• Windows 2000, Windows XP Professional, Windows Server 2003 on 32-bit x86 processors. (Minimum system configuration: Pentium III with 512MB of RAM, 350Mb disk space on C or D drive.)
• Solaris 8 or Solaris 9 on 32-bit SPARC processors (Minimum system configuration: 350Mb disk space)
• Microsoft Terminal Services support
• 5X free disk to data (minimum), 10X recommended