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💹 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.
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
KMeans and KMeans++ in Spark
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 in TypeScript
K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python
Stanford Scalable K-Means++ implementation in C++ with benchmarking.
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.
A clustering (object categorization) algorithm, with an implementation of K-means and K-means++
An implementation of K-Means clustering algorithm along with the K-Means++ seeding technique from scratch using NumPy.
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.
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?
Typescript로 구현해 보는 KMeans
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.
Neighbor Search and Clustering for Vectors using Locality-sensitive hashing and Randomized Projection to Hypercube
This is a port of the scalable k-means++ (k-means||) to the OpenMPI framework
A small, header-only, parallel implementation of kmeans clustering for arbitrary-long byte vectors.
Customers RFM Clustering (Market Segmentation based on Behavioral Approach)
Brain tumor segmentation using unsupervised methods (K means++ clustering) with morphology operation for postprocessing
Contains various machine learning algorithms and their implementations.
Clustering credit card customers with K-Means, PCA, and Hopkins statistic for segmentation and evaluation
K-means algorithm from scratch using Python.
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.
K-Means Algorithm implemented using sequential and parallel algorithms.
Flora Genie is a personalized plant recommendation system designed to help amateur gardeners select the most suitable plants for their homes or gardens.
K-Means Clustering and Gradient Descent Variants in Spark
Exploring K-means clustering through image color compression and high-dimensional data analysis. Learn how pixel grouping in RGB space builds intuition, while inertia/silhouette scores optimize clusters. Demonstrates K-means' power to reveal patterns in both visual and abstract data by optimizing groupings and selecting ideal k-values.
Data Science for Supermarket Customer Retention