GitHub - mike-liuliu/gl_index: Source code of the paper "A New Index for Clustering Evaluation Based on Density Estimation."

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Source code of the paper "A New Index for Clustering Evaluation Based on Density Estimation."

arxiv.org/abs/2207.01294

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Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
data		data
funcs		funcs
results of all 145 data		results of all 145 data
.DS_Store		.DS_Store
.gitattributes		.gitattributes
Data sources of the 145 datasets.txt		Data sources of the 145 datasets.txt
README.txt		README.txt
calculate gl_index.ipynb		calculate gl_index.ipynb
requirements.txt		requirements.txt
test gl_index - Bayesian Gaussian Mixture version.ipynb		test gl_index - Bayesian Gaussian Mixture version.ipynb
test gl_index.ipynb		test gl_index.ipynb

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0. This is the source code of the paper "A New Index for Clustering Evaluation Based on Density Estimation."

1. In function index_plot_first_n_label_one_data, if the index's score is "smaller is better", then the "smaller_better" hyper-parameter should be set to True. Otherwise, if the index's score is "larger is better", then the "smaller_better" hyper-parameter should be set to False.

2. Readers can test their own index function, the API is:
   
    def index_function(X, label):
   
    some codes to compute the index value ...
    
    return the_index_value

then call the index_plot_first_n_label_one_data function. Note the "smaller_better" hyper-parameter.

3. License.
   
License of the source code : Apache License, Version 2.0
License of new data: Creative Commons Attribution 4.0 International

4. Citation:

@article{liu2022new,
  title={A new index for clustering evaluation based on density estimation},
  author={Liu, Gangli},
  journal={arXiv preprint arXiv:2207.01294},
  year={2022}
}

5. The "multiple_label_145.p" file is larger than 100MB, so it is stored on Git Large File Storage (LFS), readers may need to download it separately.

6. Performance of other indices like Silhouette coefficient (SC), Calinski Harabasz index (CH) etc., can be found at:
https://github.com/mike-liuliu/Min-Max-Jump-distance/tree/main