• Skip to primary navigation
  • Skip to content
  • Skip to footer
Numerics.NET
Try Free Buy Now Find Anything Sign In Account
  • Welcome
  • .NET Libraries
  • Write Great Code
  • 30 Day Free Trial
  • Pricing
  • Your account
  • QuickStart Samples
  • Nuget Packages
  • Documentation
    • User's Guide and Reference
    • What's New
    • Release Notes
    • In depth: Tensors
      • Version 8.1
        • Deployment Guide
        • Nuget packages
        • Configuration
        • Using Parallelism
        • Mathematics Library User's Guide
        • Vector and Matrix Library User's Guide
        • Data Analysis Library User's Guide
        • Statistics Library User's Guide
          • Statistical Variables
          • Numerical Variables
          • Statistical Models
          • Regression Analysis
          • Analysis of Variance
          • Time Series Analysis
          • Multivariate Analysis
            • Hierarchical Cluster Analysis
            • K-Means Cluster Analysis
            • Principal Component Analysis
            • Factor Analysis
            • Discriminant Analysis
            • Partial Least Squares
          • Continuous Distributions
          • Discrete Distributions
          • Multivariate Distributions
          • Kernel Density Estimation
          • Hypothesis Tests
          • Appendices
        • Data Access Library User's Guide
        • Reference
  • Contact Us
  • Blog

This is documentation for an old release of Numerics.NET (version 8.1). Read this page in the documentation of the latest stable release (version 9.0).

  1. Home
  2. Documentation
  3. Version 8.1
  4. Statistics Library User's Guide
  5. Multivariate Analysis
C# Visual Basic C++/CLI F#

Multivariate Analysis

On this page

Numerics.NET includes a series of classes for performing multivariate data analysis on a set of data, including hiearchical and K-means cluster analysis, Factor Analysis, and Principal Component Analysis (PCA). These classes reside in the Extreme.Statistics.Multivariate namespace.

  • Hierarchical Cluster Analysis
  • K-Means Cluster Analysis
  • Principal Component Analysis
  • Factor Analysis
  • Discriminant Analysis
On this page

This website uses cookies to improve your experience. Learn more.

Got it!