Clustering
Showing 29 of 65 projects
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
Unified ggplot2 interface for visualizing statistical results from popular R packages.
An asynchronous Rust client for Valkey and Redis with support for RESP2/RESP3, clustering, TLS, and advanced features.
A Spark Streaming library for mining big data streams with incremental learning algorithms.
A Python meta-library for community detection in complex networks, implementing algorithms, fitness functions, and visualization.
An R package for creating interactive, cluster-based heatmaps using plotly for online publishing and data exploration.
A real-time online machine learning library built on Apache Storm for scalable stream processing with incremental algorithms.
A tool for automatic analysis of malware behavior using machine learning to identify, cluster, and classify malicious software.
A Julia package providing comprehensive clustering algorithms and validation metrics for data analysis.
A distributed actor framework for Go that enables building scalable, reactive systems with typed messages and clustering.
An R package for visualizing correlation matrices with automatic variable reordering to reveal hidden patterns.
A high-performance Rack-compatible HTTP server built on Vert.x for JRuby applications.
A high-performance, large-scale statistical machine learning library written in Common Lisp.
An idiomatic Clojure machine learning library providing a unified interface for classification, regression, and unsupervised models.
A high-performance, high-precision multithreaded StatsD server written in Rust with clustering and fault tolerance.
A .NET Core implementation of the Raft consensus algorithm, designed for building distributed systems without external dependencies.
A universal process manager built in Deno to keep scripts, applications, and services alive across platforms.
A Docker-based template for quickly developing clustered Elixir applications with cloud-native defaults.
A simple machine learning framework written in Swift, currently focusing on regression algorithms.
A machine learning library for Clojure built on top of Weka, providing filters, classifiers, regression, and clustering algorithms.
A D3 scale that clusters continuous data into discrete groups using a 1D clustering algorithm, similar to quantile scales.
A blazing fast specialized time-series database optimized for IoT, real-time connected devices, and AI analytics.
A Scalding library for machine learning and statistical analysis, featuring Mahout vector integration, K-Means clustering, and Naive-Bayes classifiers.
A Ruby implementation of k-means clustering with k-means++ initialization, silhouette scoring, and multiple runs for optimal results.
A Torch package providing unsupervised learning modules and algorithms like autoencoders, PCA, and k-means.
A Python pipeline for multilingual text clustering using Latent Dirichlet Allocation with stop words removal, n-gram features, and inverse stemming.
A pattern recognition library for Go providing classification, clustering, and feature extraction algorithms.
A Clojure library providing machine learning algorithms with simple APIs for data preprocessing and modeling.
A JRuby gem providing Ruby interfaces for Weka's machine learning and data mining algorithms.
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