pdc-dp-means
dpmmpythonStreaming
| pdc-dp-means | dpmmpythonStreaming | |
|---|---|---|
| 2 | 3 | |
| 15 | 14 | |
| - | - | |
| 8.1 | 0.0 | |
| 3 months ago | over 1 year ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pdc-dp-means
-
[P] Looking for state of the art clustering algorithms
Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation.
dpmmpythonStreaming
-
[P] Looking for state of the art clustering algorithms
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
-
[R] Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Code (Python wrapper): https://github.com/BGU-CS-VIL/dpmmpythonStreaming
-
[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
What are some alternatives?
cuml - cuML - RAPIDS Machine Learning Library
DeepDPM - "DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
DPMMSubClusters_GPU - DPMM Sub Clusters C++ on GPU Cross Platforms (Windows & Linux)
cudf - cuDF - GPU DataFrame Library
Point-Processes - This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.
DPMMSubClustersStreaming.jl - Code for our AISTATS '22 paper "Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data"
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)
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)