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Merge pull request #111 from PyDataBlog/revert-110-revert-108-stable-release
Revert "Revert "Updated benchmark files. Table & Plot left""
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‎.gitignore

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.idea/*
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.vscode/*
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test/experiments.jl
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/extras/.ipynb_checkpoints/*
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/extras/.ipynb_checkpoints/*
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.Rproj.user

‎Project.toml

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name = "ParallelKMeans"
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uuid = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
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authors = ["Bernard Brenyah", "Andrey Oskin"]
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version = "0.2.2"
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version = "1.0.0"
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[deps]
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Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"

‎README.md

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[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://PyDataBlog.github.io/ParallelKMeans.jl/stable)
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[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://PyDataBlog.github.io/ParallelKMeans.jl/dev)
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[![Build Status](https://github.com/PyDataBlog/ParallelKMeans.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/PyDataBlog/ParallelKMeans.jl/actions/workflows/CI.yml/badge.svg)
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[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
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[![Build Status](https://github.com/PyDataBlog/ParallelKMeans.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/PyDataBlog/ParallelKMeans.jl/actions)
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[![codecov](https://codecov.io/gh/PyDataBlog/ParallelKMeans.jl/branch/master/graph/badge.svg?token=799USS6BPH)](https://codecov.io/gh/PyDataBlog/ParallelKMeans.jl)
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[![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2FPyDataBlog%2FParallelKMeans.jl.svg?type=shield)](https://app.fossa.com/projects/git%2Bgithub.com%2FPyDataBlog%2FParallelKMeans.jl?ref=badge_shield)
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[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/PyDataBlog/ParallelKMeans.jl/master)

‎docs/src/benchmark_image.png

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‎docs/src/index.md

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- [X] Support for weighted K-means.
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- [X] Support of MLJ Random generation hyperparameter.
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- [X] Implementation of [Mini-batch KMeans variant](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf)
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- [X] Add contribution guidelines.
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- [ ] Support for other distance metrics supported by [Distances.jl](https://github.com/JuliaStats/Distances.jl#supported-distances).
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- [ ] Implementation of [Geometric methods to accelerate k-means algorithm](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf).
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- [ ] Native support for tabular data inputs outside of MLJModels' interface.
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- [ ] GPU support?
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- [ ] Distributed calculations support.
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- [ ] Optimization of code base.
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- [ ] Improved Documentation
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- [ ] Further optimization of code base.
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- [ ] Improved Documentation with more tutorials.
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- [ ] More benchmark tests.
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- [ ] GPU support?
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## How To Use
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- [Elkan()](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf) - Recommended for high dimensional data.
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- [Yinyang()](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ding15.pdf) - Recommended for large dimensions and/or large number of clusters.
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- [Coreset()](http://proceedings.mlr.press/v51/lucic16-supp.pdf) - Recommended for very fast clustering of very large datasets, when extreme accuracy is not important.
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- [MiniBatch()](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) - Recommended for extremely large datasets, when extreme accuracy is not important.
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- [MiniBatch()](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) - Recommended for extremely large datasets, when extreme accuracy is not important. *Experimental Implementation*
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- [Geometric()](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf) - (Coming soon)
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### Practical Usage Examples
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_________________________________________________________________________________________________________
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|1 million sample (secs)|100k sample (secs)|10k sample (secs)|1k sample (secs)|package |language |
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|:---------------------:|:----------------:|:---------------:|:--------------:|:---------------------:|:---------:|
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| 538.53100 | 33.15700 | 0.74238 | 0.01710 | Clustering.jl | Julia |
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| 220.35700 | 20.93600 | 0.82430 | 0.02639 | mlpack |C++ Wrapper|
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| 20.55400 | 2.91300 | 0.17559 | 0.00609 | Lloyd | Julia |
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| 11.51800 | 0.96637 | 0.09990 | 0.00635 | Hamerly | Julia |
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| 14.01900 | 1.13100 | 0.07912 | 0.00646 | Elkan | Julia |
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| 9.97000 | 1.14600 | 0.10834 | 0.00704 | Yinyang | Julia |
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| 1,430.00000 | 146.00000 | 5.77000 | 0.34400 | Sklearn KMeans | Python |
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| 30.10000 | 3.75000 | 0.61300 | 0.20100 |Sklearn MiniBatchKMeans| Python |
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| 218.20000 | 15.51000 | 0.73370 | 0.01947 | Knor | R |
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|1 million sample (secs)|100k sample (secs)|10k sample (secs)|1k sample (secs)|package |language |process |
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|-----------------------|------------------|-----------------|----------------|------------------------|-----------|----------|
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|282.7 |15.27 |0.7324 |0.01682 |Knor |R |full scan |
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|854 |87 |6.11 |0.000719 |Sklearn KMeans |Python |full scan |
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|11.2 |1.41 |0.000317 |0.000141 |Sklearn MiniBatch Kmeans|Python |stochastic|
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|254.481 |18.517 |0.000794956 |0.000031211 |Mlpack |C++ Wrapper|full scan |
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|653.178 |45.468 |0.000824115 |0.000017301 |Clustering.jl |Julia |full scan |
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|19.955 |2.758 |0.000166957 |0.000009206 |ParallelKMeans Lloyd |Julia |full scan |
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|11.234 |1.654 |0.000109074 |0.000012819 |ParallelKMeans Hamerly |Julia |full scan |
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|19.394 |1.436 |0.000109262 |0.000013726 |ParallelKMeans Elkan |Julia |full scan |
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|14.080 |0.000972914 |0.000095325 |0.000009802 |ParallelKMeans YingYang |Julia |full scan |
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_________________________________________________________________________________________________________
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- 0.1.8 Minor cleanup
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- 0.1.9 Added travis support for Julia 1.5
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- 0.2.0 Updated MLJ Interface
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- 0.2.1 Mini-batch implementation
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- 0.2.1 Initial Mini-batch implementation
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- 0.2.2 Updated MLJInterface
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- 1.0.0 Stable public release
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## Contributing
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Ultimately, we see this package as potentially the one-stop-shop for everything related to KMeans algorithm and its speed up variants. We are open to new implementations and ideas from anyone interested in this project.
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Detailed contribution guidelines will be added in upcoming releases.
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<!--- TODO: Contribution Guidelines --->
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This project adopts the [ColPrac community guidelines](https://github.com/SciML/ColPrac).
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```@index
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```

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