CVDD

Version 1.0 (827 KB) von Lianyu Hu
An Internal Validity Index Based on Density-Involved Distance
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Aktualisiert 22 Okt 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"

Zitieren als

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

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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Kompatibilität der MATLAB-Version
Erstellt mit R2016a
Kompatibel mit R2012a und späteren Versionen
Plattform-Kompatibilität
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Version Veröffentlicht Versionshinweise
1.0

Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.
Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.