Hierarchical clustering in JavaScript

Python implements three clustering algorithms: kmeans, dbscan and agnes.

AI Data Analyst — open-source data distribution platform for AI analytical systems

CS5228_Assignment1_a_Clustering including K-Means, DBSCAN, AGNES

A collection of Machine Learning and Data Mining labs for the ML course taught at INSAT.

One-click AI video generator powered by Agnes AI. Turn text ideas into multi-scene videos with Web UI. Supports text-to-video, image-to-video, and keyframes chaining.

A web app designed to conduct both offline and online clustering experiments using a dataset of archaeological sites in Vienna.

Template repository for deploying Agnes (AI Data Analyst) per customer

data mining class project for DNA sequences's clustering. Released on 2019.

A short project about customer segmentation using K-means and AGNES on a marketing dataset

Benchmark LLM reasoning agents with reproducible system-level evaluation, sandboxed code execution, tool use, and full trajectory logging

A comparison on different clustering algorithms using different datasets with performance measurements is shown here.

Implementation of AGNES algorithm

Customer segmentation is essential for enhancing marketing efficiency and satisfaction. By categorizing customers based on demographics, interests, and purchasing behavior, companies tailor messages to engage each segment effectively. Our app utilizes advanced clustering algos like KMeans, DBSCAN, and AGNES to extract insights from data

Application de bureau pour l'analyse et la comparaison d'algorithmes de Machine Learning (KNN, SVM, Réseaux de Neurones, etc.) avec une GUI interactive.

Public sample marketplace for Agnes demo instances (synthetic example plugins)

Agglomerative clustering is a "bottom-up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.

Opponent Finder Autohotkey Script for Umamusume: Pretty Derby

A collection of Unsupervised Machine Learning algorithms in Ruby

Implementation of clustering