Kmeans_pytorch
kmeans using PyTorch
Overview:
The K Means using PyTorch is an implementation of the K-means clustering algorithm in the PyTorch framework. It leverages the power of GPU to accelerate the matrix computations used in the clustering process. With support for both euclidean and cosine distances, it is suitable for clustering large datasets with flexible distance measures. The implementation is based on the style of a referenced implementation and is accompanied by thorough documentation using the jekyllbook theme. It is released under the MIT license.
Features:
- GPU acceleration: Utilizes GPU to perform faster matrix computations, improving the efficiency of the clustering process.
- Support for large datasets: Ideal for clustering large number of samples, making it suitable for big data applications.
- Distance measures: Supports both euclidean and cosine distances, providing flexibility in choosing the appropriate distance metric for different clustering tasks.
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