Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.

a visualization method for neural data

Matlab implementation for k-Shape

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

Hierarchical self-organizing maps for unsupervised pattern recognition

nQuantCpp includes top 6 color quantization algorithms for visual c++ producing high quality optimized images.

Clusteval provides methods for unsupervised cluster validation

A Pytorch Implementations for Various Vector Quantization Methods

e企查 | 金融科技服务平台企业数据的无监督分类系统-2020年第十一届中国大学生服务外包创新创业大赛A10赛题

Customer segmentation using k-modes unsupervised clustering

Forecasting and spatio-temporal clustering of criminal activity in NYC.

An implementation of Principal Component Analysis for MNIST dataset, and visualization

Team Capybara final project "Histopathologic Cancer Detection" for the Statistical Machine Learning course @ University of Trieste

There are many studies done to detect anomalies based on logs. Current approaches are mainly divided into three categories: supervised learning methods, unsupervised learning methods, and deep learning methods. Many supervised learning methods are used for log-based anomaly detection.

Clustering similar tweets using K-means clustering algorithm and Jaccard distance metric

Apply a clustering tool based on self-organizing-map to identify open clusters

MS Yang, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961

Implementation of a d3.js Visual Analytics dashboard for Sales Analysis and Customer Segmentation in Retail

Demo: how to apply Gaussian mixture modelling to Argo float data