CVDD

Version 1.0 (827 KB) by Lianyu Hu
An Internal Validity Index Based on Density-Involved Distance
24 Downloads
Updated 22 Oct 2021

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"

Cite As

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

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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MATLAB Release Compatibility
Created with R2016a
Compatible with R2012a and later releases
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes
1.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.