ClusterValidityIndices.jl

At a glance

• Editor score
  3.0 / 10
• Pricing
  Free plan
• Best for
  Julia users evaluating clustering results
• Free plan
  Yes
• Paid from
  Free
• Deployment
  Desktop
• Facts checked
  24 Sep 2026

• Where it wins

  • Evaluates cluster validity in both batch and incremental forms
  • Includes Calinski-Harabasz, Silhouette, Davies-Bouldin, and other indices
  • Open-source MIT package installable through Pkg or JuliaHub

• Where it doesn't

  • Limited to clustering evaluation rather than general statistical analysis
  • Works with feature matrices and integer cluster-label vectors
  • Julia package rather than a hosted service, with community and documentation support

Our verdict on ClusterValidityIndices.jl

ClusterValidityIndices.jl is an open-source Julia package for evaluating unsupervised clustering without ground-truth labels. It is aimed at Julia users who need to calculate cluster-validity criteria from feature matrices and integer cluster-label vectors, whether they are comparing completed clustering results or updating evaluations incrementally. Its role is specific: it addresses clustering evaluation, not a broad range of statistical-analysis tasks.

The package’s main strength is its set of indices and two evaluation modes. The documented metrics include Calinski-Harabasz, centroid-based Silhouette, Davies-Bouldin, Generalized Dunn’s Index 43 and 53, Partition Separation, WB, Xie-Beni, and Renyi representative Cross Information Potential. Batch and online evaluation are both supported, and get_cvi! is the public evaluation API. That gives Julia workflows options for comparing partitions and for incremental assessment without changing to a general-purpose statistics product. It is a better fit when these named criteria matter than when a team needs a wider analysis environment.

ClusterValidityIndices.jl is distributed as a Julia package, with installation through Pkg or JuliaHub, and is available for Linux, Windows, and macOS. It is free and open source under the MIT license, with a self-hosted deployment model; the published product information describes community and documentation support. The narrow input format and Julia-specific package form are meaningful constraints for users whose work does not already fit that ecosystem. Julia users focused on clustering criteria should consider it; people seeking broader statistical analysis or a hosted deployment should look elsewhere.

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ClusterValidityIndices.jl pricing

ClusterValidityIndices.jl fact sheet

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update