viasckde 0.1.2

VIASCKDE: internal cluster validity index (KDE-weighted compactness & separation)

VIASCKDE Index

VIASCKDE is a novel internal cluster validity index for arbitrary-shaped clusters based on Kernel Density Estimation (KDE).

---

Motivation

The VIASCKDE Index was proposed in:

"VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary Shaped Clusters Based on Kernel Density Estimation"
by Ali Şenol

The index evaluates clustering quality regardless of cluster shape by computing compactness and separation at the point level rather than relying on centroids.

---

Installation

pip install viasckde
Usage Example
python
Copy code
from viasckde import viasckde_score

# X: 2D or n-D data array (NumPy-like)
# labels: predicted cluster labels
score = viasckde_score(X, labels)
print("VIASCKDE Score:", score)
Parameters
Parameter	Description
X	Dataset, 
𝑋
=
{
𝑥
1
,
.
.
.
,
𝑥
𝑛
}
∈
𝑅
𝑑
X={x 
1
​
 ,...,x 
n
​
 }∈R 
d
 
labels	Cluster labels (array-like)
kernel	KDE kernel — default: gaussian
bandwidth	KDE bandwidth — default: 0.05

Recommended values:

Kernel: gaussian

Bandwidth: 0.05

Output Range
The VIASCKDE score lies in [-1, +1], where:

+1 → best clustering

-1 → worst clustering

Citation
If you use VIASCKDE in your research, please cite:

Ali Şenol, “VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation”,
Computational Intelligence and Neuroscience, vol. 2022, Article ID 4059302, 20 pages, 2022.
https://doi.org/10.1155/2022/4059302

BibTeX
bibtex
Copy code
@article{csenol2022viasckde,
  title={VIASCKDE Index: A Novel Internal Cluster Validity Index for Arbitrary-Shaped Clusters Based on the Kernel Density Estimation},
  author={{\c{S}}enol, Ali},
  journal={Computational Intelligence and Neuroscience},
  volume={2022},
  number={1},
  pages={4059302},
  year={2022},
  publisher={Wiley Online Library}
}
License & Author
Author: Assoc. Prof. Dr. Ali Şenol
Computer Engineering Department, Tarsus University

License: MIT