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The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.
Library of data wrangling functions that an internal auditor typically needs (for my own use and learning, if you wish to use or collaborate pls get in touch, or use at your own peril).
This repository provides classic clustering algorithms and various internal cluster quality validation metrics and also visualization capabilities to analyse the clustering results