FFCL: Flexible and (probably not the) fast(est) c++ clustering library.

UR3 CobotOps dataset

A KMeans implemented in C++ with Python bindings and GPU acceleration

Optimal univariate k-means clustering using dynamic programming

python-for-datascience-cheatsheet

Implementation of K-means,Bisecting K-means and Decision Tree in PySpark on the Iris Dataset.

Plain python implementations of basic machine learning algorithms

ML projects using a variety of different methods for solving classification problems

Football Match Analysing Tool

This is a recomendation system based on clustering analysis. After clustering a user_profiles.json file wil be generated keeping the users preferences. This system generates personalized recommendations, based on each user's preferences and other users preferences that belong in the same cluster.

Efficient Implementation of Kmeans++ Algorithm

BICO is a fast streaming algorithm to compute coresets for the k-means problem on very large sets of points.

Histogram_Equalization & K-Means iOS App

An investingation into London FIre Brigade's callout data.

Color Detection using K-means clustering algorithm to detect and recognize dominant color from images.

Scan Tailor Experimental is an interactive post-processing tool for scanned pages.

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

The K-Means Visualizer is an interactive web application designed to help users understand and visualize the K-Means clustering algorithm. Through an intuitive interface, users can experiment with different numbers of data points and clusters, and observe how the algorithm iteratively updates centroids and assigns data points to clusters.

A CLI tool to find the dominant colours in an image 🎨

This notebook provides a comprehensive example of how to perform customer segmentation using K-Means clustering, including data preprocessing, visualization, standardization, one-hot encoding, model training, evaluation, and saving/loading the model.