- Home
- Statistical Analysis Software
- ClusterValidityIndices.jl
On the recordRead 12 Oct 202622 facts
ClusterValidityIndices.jl
On the record
- Batch and incremental modes
- The package implements both batch and incremental variants, but one CVI object cannot switch between those modes.ap6yc.github.io · read 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 · read 12 Oct 2026
- Data format
- The package expects floating-point feature matrices with samples in columns and integer cluster labels.ap6yc.github.io · read 12 Oct 2026
- Data import formats
- Float matrices with integer cluster-label vectorsap6yc.github.io · read 24 Sep 2026
- Deployment
- desktopap6yc.github.io · read 24 Sep 2026
- Distribution
- The project is distributed as a Julia package through JuliaHub and can also be installed directly from GitHub.ap6yc.github.io · read 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 · read 12 Oct 2026
- Examples
- The repository says its example scripts can be opened, saved, and run as Julia notebooks.github.com · read 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.ap6yc.github.io · read 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 · read 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 · read 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 · read 12 Oct 2026
- Installation
- The package can be installed through Julia's package manager or directly from its GitHub repository.ap6yc.github.io · read 12 Oct 2026
- Intended users
- The package is intended for evaluating unsupervised clustering algorithms using data samples and algorithm-assigned cluster labels.github.com · read 12 Oct 2026
- License
- The repository states that the software is maintained under the MIT License.github.com · read 12 Oct 2026
- Maintainer
- The contribution guide names Sasha Petrenko as the current developer and maintainer.ap6yc.github.io · read 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 · read 12 Oct 2026
- Purpose
- The package provides cluster validity indices to assess unsupervised clustering when ground-truth labels are unavailable.ap6yc.github.io · read 12 Oct 2026
