ClusterValidityIndices.jl
The facts below are what the maker publishes about ClusterValidityIndices.jl, checked and dated. Our editors have not written about it yet.
The facts
Each one read from ClusterValidityIndices.jl's own pages, with the date it was checked.
What it does
- Batch and incremental modes
- The package implements both batch and incremental variants, but one CVI object cannot switch between those modes.checked 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.checked 12 Oct 2026
- Data format
- The package expects floating-point feature matrices with samples in columns and integer cluster labels.checked 12 Oct 2026
- Data import formats
- Float matrices with integer cluster-label vectorschecked 24 Sep 2026
- Deployment
- desktopchecked 24 Sep 2026
- Distribution
- The project is distributed as a Julia package through JuliaHub and can also be installed directly from GitHub.checked 12 Oct 2026
- Documentation build
- The official documentation reports that it was built with Julia 1.8.5 on Linux on January 13, 2023.checked 12 Oct 2026
- Examples
- The repository says its example scripts can be opened, saved, and run as Julia notebooks.checked 12 Oct 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.checked 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.checked 12 Oct 2026
- Incremental evaluation
- Incremental cluster validity indices support evaluating a clustering process online and are numerically equivalent to their batch counterparts.checked 12 Oct 2026
- Input format
- The guide expects floating-point data matrices with samples in columns and features in rows, plus integer cluster labels.checked 12 Oct 2026
- Installation
- The package can be installed through Julia's package manager or directly from its GitHub repository.checked 12 Oct 2026
- Intended users
- The package is intended for evaluating unsupervised clustering algorithms using data samples and algorithm-assigned cluster labels.checked 12 Oct 2026
- License
- The repository states that the software is maintained under the MIT License.checked 12 Oct 2026
- Maintainer
- The contribution guide names Sasha Petrenko as the current developer and maintainer.checked 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.checked 12 Oct 2026
- Purpose
- The package provides cluster validity indices to assess unsupervised clustering when ground-truth labels are unavailable.checked 12 Oct 2026
- Scripting support
- Yeschecked 24 Sep 2026
- Security and compliance
- The reviewed project pages state an MIT license but do not describe security certifications or compliance standards.checked 12 Oct 2026
- Support
- The project directs questions and requested changes to its public GitHub issues page.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 24 Sep 2026
