Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering

Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering

Jax implementation of Mini-batch K-Means algorithm

A hybrid machine learning recipe recommendation system using collaborative filtering and content-based filtering on the Food.com dataset.

Color compression of an image with K-Means Clustering Algorithm which can help in devices with low processing power and memory for large images

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.

Performing basic clustering on a seeds dataset.

Developed for "Management and Analysis of Physics Dataset Mod. B," this project uses Dask and CloudVeneto VMs to handle a massive 250GB dataset. Clustering on 800k RCV1 articles involves dataset reduction by macrocategory and also implementing cosine similarity for improved clustering, as suggested by Natural Language Processing principles.

Perbandingan Metode K-Means dan Mini-Batch K-Means menggunakan Auto-Tuning Hyperparameter untuk Klasterisasi ISPU DKI Jakarta

This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' homogenous characteristics. Influenced the bank's marketing strategies and initiatives. Developed in Jupyter Notebook with Python for FNB.