cluster-validation

A general purpose Snakemake workflow and MrBiomics module to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

Pepelka is a MATLAB toolbox for data clustering and visualization.

A python package which implements a distance-based extension of the adjusted Rand index for the supervised validation of 2 cluster analysis solutions

This folder contains code for the paper titled "Information maximization-based clustering of histopathology images using deep learning".

An R package for cluster validation.

Clustering algorithms, validations and interpretations with some robust statistic advanced topics. Real case applications. My R scripts to solve some university exams ;) - Work in progress

This repository contains the code for the conference paper titled "Feature extraction and unsupervised clustering of histopathological images of pancreatic cancer using information maximization".

An end-to-end unsupervised machine learning framework benchmarking K-Means, Agglomerative Hierarchical, and DBSCAN clustering topologies on the Iris dataset using Silhouette and Davies-Bouldin metrics.

A data-driven customer segmentation project using hierarchical (Ward.D2) and k-means clustering on retail survey data. The analysis applies z-score normalization, Euclidean distance and cluster validation (NbClust) to identify four distinct segments and translate insights into strategic targeting recommendations using the McKinsey GE Matrix.

Internship project: Decomposition of somatic mutation profiles into mutational signatures

CompSil: Composite Silhouette for Cluster-Count Selection

This repository contains the source code associated with the paper titled "Implementation of a conditional latent diffusion-based generative model to synthetically create unlabeled histopathological images".

Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage).