ClusterValidityIndices.jl

Spec sheet

Purpose

The package provides cluster validity indices to assess unsupervised clustering when ground-truth labels are unavailable.Read 12 Oct 2026 · ap6yc.github.io

Intended users

The package is intended for evaluating unsupervised clustering algorithms using data samples and algorithm-assigned cluster labels.Read 12 Oct 2026 · github.com

Deployment

desktopRead 24 Sep 2026 · ap6yc.github.io

Free plan

YesRead 24 Sep 2026 · ap6yc.github.io

Support

The project directs questions and requested changes to its public GitHub issues page.Read 12 Oct 2026 · github.com

License

The repository states that the software is maintained under the MIT License.Read 12 Oct 2026 · github.com

Batch and incremental modes

The package implements both batch and incremental variants, but one CVI object cannot switch between those modes.Read 12 Oct 2026 · ap6yc.github.io

Batch and incremental use

Its indices have batch and incremental implementations for evaluating clustering results as a batch or as samples arrive.Read 12 Oct 2026 · ap6yc.github.io

Data format

The package expects floating-point feature matrices with samples in columns and integer cluster labels.Read 12 Oct 2026 · ap6yc.github.io

Data import formats

Float matrices with integer cluster-label vectorsRead 24 Sep 2026 · ap6yc.github.io

Distribution

The project is distributed as a Julia package through JuliaHub and can also be installed directly from GitHub.Read 12 Oct 2026 · ap6yc.github.io

Documentation build

The official documentation reports that it was built with Julia 1.8.5 on Linux on January 13, 2023.Read 12 Oct 2026 · ap6yc.github.io

Examples

The repository says its example scripts can be opened, saved, and run as Julia notebooks.Read 12 Oct 2026 · github.com

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.Read 12 Oct 2026 · ap6yc.github.io

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.Read 12 Oct 2026 · github.com

Incremental evaluation

Incremental cluster validity indices support evaluating a clustering process online and are numerically equivalent to their batch counterparts.Read 12 Oct 2026 · ap6yc.github.io

Input format

The guide expects floating-point data matrices with samples in columns and features in rows, plus integer cluster labels.Read 12 Oct 2026 · ap6yc.github.io

Installation

The package can be installed through Julia's package manager or directly from its GitHub repository.Read 12 Oct 2026 · ap6yc.github.io

Maintainer

The contribution guide names Sasha Petrenko as the current developer and maintainer.Read 12 Oct 2026 · ap6yc.github.io

Mode limitation

A CVI object cannot switch between batch and incremental evaluation; a fresh object is needed to use the other mode.Read 12 Oct 2026 · ap6yc.github.io

Scripting support

YesRead 24 Sep 2026 · ap6yc.github.io

Security and compliance

The reviewed project pages state an MIT license but do not describe security certifications or compliance standards.Read 12 Oct 2026 · github.com

Where Specifiction places it

• Statistical Analysis Software#102 of 1066.8