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ClusterValidityIndices.jl

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What ClusterValidityIndices.jl is

ClusterValidityIndices.jl is a free Julia package for assessing unsupervised clustering when ground-truth labels are unavailable. It accepts floating-point feature matrices with samples in columns and features in rows, together with integer cluster labels. Batch implementations evaluate completed results, while incremental implementations assess samples as they arrive; the incremental measures are numerically equivalent to their batch counterparts. Documented indices include Calinski-Harabasz, centroid-based Silhouette, Davies-Bouldin, Generalized Dunn 43 and 53, Partition Separation, representative Cross Information Potential, WB-index, and Xie-Beni. A CVI object cannot change between batch and incremental modes, so another object is needed for the other mode. The package is distributed through JuliaHub and can be installed with Julia’s package manager or from GitHub. Examples can run as Julia notebooks. The repository uses the MIT License, and GitHub Issues are the stated contact channel.

Who it suits

It suits Julia users evaluating clustering algorithms with assigned labels, either in batch workflows or as data arrives incrementally.

What it does well

  • Free Julia package
  • Batch and incremental implementations
  • Multiple documented validity indices
  • Installable through Julia package manager or GitHub
  • MIT License

What to know first

  • Input requires specific matrix and label formats
  • CVI objects cannot switch evaluation modes
  • Linux platform listed

Verdict

ClusterValidityIndices.jl provides a broad set of clustering validity measures for batch and online evaluation. Its fixed input layout and separate objects for each mode are important implementation details.

Facts from ClusterValidityIndices.jl's own pages

Batch and incremental modes
The package implements both batch and incremental variants, but one CVI object cannot switch between those modes. · ap6yc.github.io, 12 Oct 2026
Batch and incremental use
Its indices have batch and incremental implementations for evaluating clustering results as a batch or as samples arrive. · ap6yc.github.io, 12 Oct 2026
Data format
The package expects floating-point feature matrices with samples in columns and integer cluster labels. · ap6yc.github.io, 12 Oct 2026
Data import formats
Float matrices with integer cluster-label vectors · ap6yc.github.io, 24 Sept 2026
Deployment
desktop · ap6yc.github.io, 24 Sept 2026
Distribution
The project is distributed as a Julia package through JuliaHub and can also be installed directly from GitHub. · ap6yc.github.io, 12 Oct 2026
Documentation build
The official documentation reports that it was built with Julia 1.8.5 on Linux on January 13, 2023. · ap6yc.github.io, 12 Oct 2026
Examples
The repository says its example scripts can be opened, saved, and run as Julia notebooks. · github.com, 12 Oct 2026
Free plan
Yes · ap6yc.github.io, 24 Sept 2026
Implemented indices
The documented indices are Calinski-Harabasz, centroid-based Silhouette, Davies-Bouldin, Generalized Dunn 43 and 53, Partition Separation, representative Cross Information Potential, WB-index, and Xie-Beni. · ap6yc.github.io, 12 Oct 2026
Included indices
The documented indices include Calinski-Harabasz, centroid-based Silhouette, Davies-Bouldin, two generalized Dunn indices, Partition Separation, representative Cross Information Potential, WB-index, and Xie-Beni. · github.com, 12 Oct 2026
Incremental evaluation
Incremental cluster validity indices support evaluating a clustering process online and are numerically equivalent to their batch counterparts. · ap6yc.github.io, 12 Oct 2026
Input format
The guide expects floating-point data matrices with samples in columns and features in rows, plus integer cluster labels. · ap6yc.github.io, 12 Oct 2026
Installation
The package can be installed through Julia's package manager or directly from its GitHub repository. · ap6yc.github.io, 12 Oct 2026
Intended users
The package is intended for evaluating unsupervised clustering algorithms using data samples and algorithm-assigned cluster labels. · github.com, 12 Oct 2026
License
The repository states that the software is maintained under the MIT License. · github.com, 12 Oct 2026
Maintainer
The contribution guide names Sasha Petrenko as the current developer and maintainer. · ap6yc.github.io, 12 Oct 2026
Mode limitation
A CVI object cannot switch between batch and incremental evaluation; a fresh object is needed to use the other mode. · ap6yc.github.io, 12 Oct 2026
Purpose
The package provides cluster validity indices to assess unsupervised clustering when ground-truth labels are unavailable. · ap6yc.github.io, 12 Oct 2026
Scripting support
Yes · ap6yc.github.io, 24 Sept 2026
Security and compliance
The reviewed project pages state an MIT license but do not describe security certifications or compliance standards. · github.com, 12 Oct 2026
Support
The project directs questions and requested changes to its public GitHub issues page. · github.com, 12 Oct 2026

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