dpmmpythonStreaming
DPMMSubClusters_GPU
| dpmmpythonStreaming | DPMMSubClusters_GPU | |
|---|---|---|
| 3 | 1 | |
| 14 | 8 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Python | C++ | |
| MIT License | - |
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dpmmpythonStreaming
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[P] Looking for state of the art clustering algorithms
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
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[R] Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Code (Python wrapper): https://github.com/BGU-CS-VIL/dpmmpythonStreaming
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[R] DeepDPM: Deep Clustering With an Unknown Number of Clusters
We have yet to publish a streaming-data solution, but it is definitely a direction we are considering. That said, there is nothing refraining you from keep training the network. However, one should carefully design how you update the input (training) data. There is also this solution that seems interesting: https://github.com/BGU-CS-VIL/dpmmpythonStreaming
DPMMSubClusters_GPU
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[R] Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
CUDA Version (for inference on GPU's) - https://github.com/BGU-CS-VIL/DPMMSubClusters_GPU
What are some alternatives?
DeepDPM - "DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
DPMMSubClustersStreaming.jl - Code for our AISTATS '22 paper "Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data"
Point-Processes - This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.
cuml - cuML - RAPIDS Machine Learning Library
VersatileHDPMixtureModels.jl - Code for our UAI '20 paper "Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes"
dpmmpython - Python wrapper for the DPMMSubCluster Julia package for inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
pdc-dp-means - "Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation" [Dinari and Freifeld, UAI 2022]
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
cudf - cuDF - GPU DataFrame Library