CVDD

Versión 1.0 (827 KB) por Lianyu Hu
An Internal Validity Index Based on Density-Involved Distance
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Actualizado 22 oct 2021
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An Internal Validity Index Based on Density-Involved Distance

https://ieeexplore.ieee.org/document/8672850

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Requirement

The source code is written by Matlab r2016a. Versions lower than Matlab r2012a have not been tested. View CVDD on File Exchange

Simplest Demo

  • RUN Ncut_test.m to determine the optimal partition from varied partitions (produced by Jianbo Shi's Normalized cuts).

    CVDD.m includes Algorithm 1: CVDD in our paper.

    Ncut_test.m as an example includes Algorithm 2: CVDD-OP in our paper.

    'OP_CA' (in Ncut_test.m) shows the comparison. [ Purity, CVDD, CVNN, WB, Silhouette, CH, DB, Dunn, S_Dbw, I ]

Parameters in CVDD

No need to tune.

Datasets used

File Datasets_all30 includes 10 non-spherical clusters, 10 spherical clusters and 10 classification datasets (real datasets) used in the experiments of our paper.

Issues, Questions, etc

Please report issues here on the github page or contact "hly4ml@gmail.com"

Citar como

Lianyu Hu (2024). CVDD (https://github.com/hulianyu/CVDD/releases/tag/v1.0), GitHub. Recuperado .

Hu, Lianyu, and Caiming Zhong. An Internal Validity Index Based on Density-Involved Distance. Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 40038–51, doi:10.1109/access.2019.2906949.

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Compatibilidad con la versión de MATLAB
Se creó con R2016a
Compatible con cualquier versión desde R2012a
Compatibilidad con las plataformas
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Versión Publicado Notas de la versión
1.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.