💹 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.

No description provided.

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

Neighbor Search and Clustering for Time-Series using Locality-sensitive hashing and Randomized Projection to Hypercube. Time series comparison is performed using Discrete Frechet or Continuous Frechet metric.

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

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

A clustering (object categorization) algorithm, with an implementation of K-means and K-means++

I explore and compare different techniques for unsupervised scene segmentation. I try to answer these research questions: 1.) Can unsupervised convolutional neural networks learn enough structure from data to generate good quality segments? 2.) Is spatial continuity important to generate good quality clusters? 3.) Can we improve results from CNN and GMMs using K-means?

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.

No description provided.

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.

Jupyter notebook with Object Oriented implementation of the k-means clustering algorithm. Experimenting with both random and k-means initialization.

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

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

Typescript로 구현해 보는 KMeans

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

Customers RFM Clustering (Market Segmentation based on Behavioral Approach)

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.

This project is aimed at leveraging dataset containing > 500K credit card transaction in Europe in 2023 to train a ML model to predict/detect fraudulent transactions.

An implementation of K-Means clustering algorithm along with the K-Means++ seeding technique from scratch using NumPy.

K-Means Algorithm implemented using sequential and parallel algorithms.

K-means algorithm from scratch using Python.

Contains various machine learning algorithms and their implementations.

Clustering credit card customers with K-Means, PCA, and Hopkins statistic for segmentation and evaluation