Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.

Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.

A small-scale flask server facial recognition system, using a pre-trained facenet model with real-time web camera face recognition functionality, and a pre-trained Multi-Task Cascading Convolutional Neural Network (MTCNN) for face detection and cropping.

统计分析课程实验作业/包含《统计分析方法》中因子分析，主成分分析，Kmeans聚类等典型算法的手写实现

This project consists of implementations of several kNN algorithms for road networks (aka finding nearest points of interest) and the experimental framework to compare them from a research paper published in PVLDB 2016. You can use it to add new methods and/or queries or reproduce our experimental results.

Implementations of different algorithms for building Euclidean minimum spanning tree in k-dimensional space.

This repository is a related to all about Natural Langauge Processing - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.

A Java console application that implements the factionality of the knn algorithm to find the similarity between a new user with only a few non zero ratings of some locations, find the k nearest neighbors through similarity score and then predict the ratings of the new user for the non rated locations.

Euclidean Distance, Quantization, RGB, HSV

TextureBasedImageRetriever a Content Based Image Retriever that focuses on texture. It implements the offline phase which is the calulation of descriptors of all images in the datasetn, and the online phase that return the n-similar images from dataset given an input image.

K-Means and Bisecting K-Means clustering algorithms implemented in Python 3.

A python package to compute pairwise Euclidean distances on datasets with categorical features in little time

Sviluppo dell'algoritmo esteso di euclide. Permette all'utente di calcolare l'MCD tra due numeri interi e restituisce i coefficenti dell'identità di Bezout.

Various algorithms developed utilizing some of Irvine's Library(32-bit). Please refer to asmirvine.com for more info on installing Irvine's libraries to run the following programs.

This course teaches you how to calculate distance metrics, form and identify clusters in a dataset, implement k-means clustering from scratch and analyze clustering performance by calculating the silhouette score

Collection of Generic Data Structures and Algorithms in C.

Allows for calculation of many types of distance between points