5 packages tagged with “KMeans”
Contains Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
Simple double precision histogram of configurable number of bins, with KMeans functionality
K-Means++ implementation for the .NET platform, includes Silhoutte k-estimator
Welvet - LOOM Neural Network Framework for .NET. 12 layer types (Dense, LSTM, RNN, Conv2D, Conv1D, MHA, LayerNorm, RMSNorm, SwiGLU, Softmax, Embedding, Parallel), transformer/LLM inference with streaming, neural tweening, K-Means clustering, Pearson/Spearman correlation, network grafting, step-based forward pass, 7 LR schedulers, 3 optimizers (SGD/AdamW/RMSprop), ensemble features, and bit-for-bit model sharing with Python/TypeScript/Go. Multi-platform: Linux, Windows, macOS, Android, iOS.
High-performance .NET library for optimal 1D K-means clustering using dynamic programming. Guarantees globally optimal solutions for one-dimensional data in O(k·n) time, outperforming traditional K-means implementations by 39-951x in speed while ensuring deterministic results.