CVI_using_DSI

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Cluster Validity Index Using a Distance-based Separability Measure

CVI_using_DSI 최신버젼 다운로드

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A novel CVI: Distance-based Separability Index (DSI)
=============
This is an example of computing Cluster Validity Index (CVI) on datasets after clustering.

It includes codes to compute the distance-based separability index (DSI), which is used in another study: "Data Separability for Neural Network Classifiers and the Development of a Separability Index" ([Preprint](https://arxiv.org/abs/2005.13120)).

Related paper
=============
### An Internal Cluster Validity Index Using a Distance-based Separability Measure

`Citation` S. Guan and M. Loew, "An Internal Cluster Validity Index Using a Distance-based Separability Measure," 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), Baltimore, MD, USA, 2020, pp. 827-834, doi: 10.1109/ICTAI50040.2020.00131.

[`Paper`](https://doi.org/10.1109/ICTAI50040.2020.00131) [`Arxiv`](https://arxiv.org/abs/2009.01328) [`Video`](https://youtu.be/TfihU4uBA_8)

### Data Separability for Neural Network Classifiers and the Development of a Separability Index

[`Arxiv`](https://arxiv.org/abs/2005.13120)

Results
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### DSI scores of the [handwritten digits dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) after applying several clustering methods
Output in `results.txt`.

* 0.4933647148073444  `ture labels`
* 0.4836361123766773  `K-Means`
* 0.5716958115555282  `Spectral Clustering`
* 0.5471394199023191  `Birch`
* 0.4697710019443232  `Gaussian Mixture (EM)`

CVI_using_DSI

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