K-Means algorithm parallelized in CUDA

This repository aims to provide an overview of various clustering methods, along with practical examples and implementations.

An API for managing chat completions, fine-tuning, payments, plans, and configurations.

This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each observation is assigned to the cluster with the nearest mean, serving as a prototype of the cluster.

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

In this Python notebook, we explore how K-Means can be used for customer segmentation to gain a competitive advantage and improve a business's bottom line.

This repo contains the Implementation of K-Means Clustering Algorithm from scratch and an Image Segmentation Project, implemented using the same algorithm.

A C implementation of K-Means clustering algorithm with Python bindings

Customer Segmentation using R

K-means clustering algorithm using MapReduce.

This Repo contains various Machine learning Algorithm including Linear regression, Logistic regression, Neural Networks, SVM, Clustering algorithms, K-means Algorithm, Anomaly detection, and Recommander system etc...

This repository contains an example of using K-means clustering to partition data into distinct groups based on similarity.

An analysis using unsupervised Machine Learning algorithm to discover unknown patterns

K-means algorithm is implemented from scratch for clustering on iris dataset and MNIST dataset.

Implementation of k-means algorithm in C (standard C89/C90)

Cluster Visualization Tool

The K -Means algorithm implementation from scratch in Python based on Euclidean distance

In this two cluster approaches are used: hierarchical clustering and K-means clustering. It is unsupervised learning technique for grouping related data points which shows same behaviour in the dataset regardless of the outcome.