Center points of world countries - CSV and GeoJSON

Centroid-UNet is deep neural network model to detect centroids from satellite images.

Supervised Learning Recap

MICS Geocoding is a QGIS plugin for processing household survey geospatial data, enabling cluster centroids, privacy-preserving displacement, and extraction of geospatial covariates.

object detections on polygonal roi using yolo

Parallellization of the Kmeans algorithm with OpenMP

Using Supervised Machine Learning algorithms to identify credit risks

Data Analysis, EDA and Unsupervised Machine Learning Models on Uber NY Dataset

function in R for calculating a centroid matrix

The Similarity Search Tree is an efficient method for indexing high dimensional feature vectors. The main objective of this data structure is to obtain the nearest neighbors given a certain query vector in a reasonable amount of time. In this project, the k-NN algorithm was adapted for supporting image retrieval.

Um exemplo que mostra o cálculo do número e a distâncias aos centroides utilizando um dataset de flores iris.

Analyse d'un groupement de pays cible pour l'exportation de poulet (clustering, CAH, k-means, ACP)

Assistance in the analysis of dip/direction measurements of rock mass discontinuities

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

Code for the paper "DUCK: Distance-based Unlearning via Centroid Kinematics"

Análisis de datos (aprendizaje no supervisado), clasificación de los jugadores del juego FC25 en 4 clusters, aplicando 3 medidas diferentes para valorar la similitud.

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.

Clustering algorithm with other functions (Laplacian Norm, Jacobi algorithm - Eigenvalues and Eigenvectors extractor, etc)

Simple object tracking by using the centroid tracking algorithm