gesture recognition toolkit

HMM based on KMeans and GMM

Efficient similarity search and clustering for Ruby

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

A C++ implementation of simple k-means clustering algorithm.

A Python package for optimal 1D k-means clustering.

instance search of bow model framework(ins bagger)

KNN, KMeans, Decision Tree, Naive Bayesian, Linear Regression, Principal Component Analysis, Neural Networks, Support Vector Machines all written in C++ from scratch.

Source code of HG-means clustering, from "HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering". (Gribel and Vidal, 2019)

Efficient Implementation of Kmeans++ Algorithm

Shadow Detector

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

A version of the K-Means Algorithm targeting the Capacitated Clustering Problem

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

K-Means clustering in C++17: header-only sequential and parallel implementations

Linde, Buzo, and Gray algorithm for vector quantization.

Kmeans-clustering based on point-cloud data.

LOG-Means算法是一种新型、简化的、高效、对大数据集和大搜索空间具有强鲁棒性的簇数目估计方法。它采用了二分搜索策略和递归细化策略，分别在大范围和小范围内进行簇数目估计，从而高效估计数据中的簇的个数。

Program to perform color quantization using four k-means variants: 1) Batch K-Means (Forgy, 1965 and Lloyd, 1982) 2) Incremental Batch K-Means (Linde et al., 1980) 3) Online K-Means (MacQueen, 1967) 4) Incremental Online K-Means (Abernathy & Celebi, 2022) Authors: Amber Abernathy & M. Emre Celebi Contact email: ecelebi@uca.edu

Multithread open source application for k-means clustering, support really big files (lineCount <= 1000000000, dimensionsCount <=1000, centroids count <=1000)