A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.

A LibreOffice Calc extension that clusters the rows in a table and colors them to indicate the clusters.

Gaussian Mixture Model and Evaluation-Maximization Algorithm

Biometric authentication protocol using your voice

Revealing insight about forest change area in Semarang City using data mining. This is made for Computer Science Event in my campus as paper submiter.

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data

Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences

Gaussian Mixture Model with support for heterogeneous missing and censored (upper limit) data.

Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

Customer Segmentation using Clustering

Matlab functions to plot 2D and 3D maps from nanoindentation tests.

Probabilistic sequence generation of sketch drawings which builds on Google Brain's "A Neural Representation of Sketch Drawings"

R package for maximal likelihood estimation of multivariate normal mixture models

Utilising clustering algorithms like Affinity Propagation, Gaussian Mixture Models, Spectral Clustering, Fuzzy C-means, and Hierarchical Clustering to reveal customer segments and patterns in Uber Eats USA data, generating practical suggestions and visual insights.

Full scripts to generate figures for "Defining Southern Ocean fronts using unsupervised classification" https://doi.org/10.5194/os-17-1545-2021

Superiority of Quadratic Over Conventional Neural Networks for Classification of Gaussian Mixture Data

Implement of paper "Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures"

Collaborative filtering via Gaussian Mixture Models. As part of the MITx course on machine learning with Python - from linear models to deep learning

This repo contains implementations of key unsupervised learning techniques, including image compression (K-means, GMM), PCA for Eigenfaces, ICA for audio separation, and CVAE for MNIST generation. It's a resource for understanding and applying foundational algorithms.