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

• UpdatedJun 19, 2024
• R

Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering

• UpdatedMay 27, 2023
• R

Jax implementation of Mini-batch K-Means algorithm

• UpdatedOct 29, 2022
• Python

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

• UpdatedSep 5, 2018
• Jupyter Notebook

• UpdatedOct 15, 2021
• Jupyter Notebook

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.

• UpdatedJul 9, 2023
• C++

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.

• UpdatedJan 25, 2024
• HTML

Performing basic clustering on a seeds dataset.

• UpdatedOct 25, 2023
• Jupyter Notebook

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.

• UpdatedAug 20, 2021
• Jupyter Notebook