💹 K-Means clustering implementation in TypeScript

Develop a customer segmentation to define marketing strategy. Used PCA to reduce dimensions of the dataset and KMeans++ clustering technique is used for clustering and profiling of clusters.

Implementation of the FLS++ algorithm for K-Means clustering.

This is an end-to-end project that focuses on predicting credit card default using machine learning techniques. The project includes data validation,data preprocessing, model training, evaluation, and deployment.

k-means / k-means++ / elbow-method

Green Space Design Company Team Assignment

k-means clustering in TypeScript

KMeans and KMeans++ in Spark

Stanford Scalable K-Means++ implementation in C++ with benchmarking.

K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python

A small, header-only, parallel implementation of kmeans clustering for arbitrary-long byte vectors.

Explore my solo Customer Segmentation Project, diving into data analysis, clustering, and visualization. Uncover distinct customer segments for tailored marketing strategies and enhanced engagement. Discover the power of data-driven insights in this independent project.

Fair K-Means produces a fair clustering assignment according to the fairness definition of Chierichetti et al. Each point has a binary color, and the goal is to assign the points to clusters such that the number of points with different colors in each cluster is the same and the cost of the clusters is minimized.

Brain tumor segmentation using unsupervised methods (K means++ clustering) with morphology operation for postprocessing

KMeans With UI Interaction은 클릭 혹은 터치 이벤트를 통해 생성된 포인트 형태의 데이터 집합을 사용하여 KMeans++ Clustering을 진행하는 일련의 과정을 경험해 볼 수 있는 웹 서비스 입니다.

This is a port of the scalable k-means++ (k-means||) to the OpenMPI framework

Customers RFM Clustering (Market Segmentation based on Behavioral Approach)

Go library implementing Kmeans++ and Elkan's Kmeans algorithm