Insightful Miner 8

Make Better Decisions for Better Business Results

Insightful Miner is a powerful, scalable, data mining and analysis workbenchthat enables organizations to delivercustomized predictive intelligence where and how it is needed.

Its easy-to-use interface is specifically designed for statisticians and business analysts without specialized programming skills. With Insightful Miner, you can quickly find the answers you need to solve specific business issues and easily communicate your results to colleagues across the organization.

Features of Insightful Miner 8

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

Data Access (Input and Output)

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
Naļve 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 8 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-PLUSon 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

Systems Requirements

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

Insightful Miner 8

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