andclust 1.0.0

ANDClust: Adaptive Neighborhood Density-Based Clustering Algorithm

ANDClust

ANDClust is a clustering algorithm based on Adaptive Neighborhood Density and MST expansion with local density ratios.
This package implements the final optimized version of the ANDClust algorithm.

Installation

pip install andclust

Usage

from andclust import ANDClust
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics.cluster import adjusted_rand_score

data=load_iris()
X,y=data['data'],data['target']

scaler = MinMaxScaler()
scaler.fit(X)
X = scaler.transform(X)

model = ANDClust(N=2,k=14,eps=0.113) # If you want to change kernel and band_with use model = ANDClust(N=2,k=14,eps=0.113,kernel='gaussian',b_width=0.025) default values for optional parameter krnl='gaussian', b_width=0.5 options for kernel are{“gaussian”, “tophat”, “epanechnikov”,
“exponential”, “linear”, “cosine”}
labels = model.fit_predict(X)

ARI=adjusted_rand_score(labels,y)
print("ARI=", ARI)
model.plotGraph("ARI",ARI,dataset_name)

Features

Adaptive neighborhood density (AND)

Kernel Density Estimation–based cluster core detection

MST expansion using local ratio constraints

Noise handling

High performance (KDTree + vectorized operations)

##Citation

If you use this algorithm in research, please cite the corresponding paper.

Şenol, A. (2024). ANDClust: An Adaptive Neighborhood Distance-Based Clustering Algorithm to Cluster Varying Density and/or Neck-Typed Datasets. Advanced Theory and Simulations, 7(4), 2301113.

#BibTeX

@article{csenol2024andclust,
  title={ANDClust: An Adaptive Neighborhood Distance-Based Clustering Algorithm to Cluster Varying Density and/or Neck-Typed Datasets},
  author={{\c{S}}enol, Ali},
  journal={Advanced Theory and Simulations},
  volume={7},
  number={4},
  pages={2301113},
  year={2024},
  publisher={Wiley Online Library}
}

---

LICENSE **

MIT License

Copyright (c) 2025 Ali Şenol

Permission is hereby granted, free of charge, to any person obtaining a copy
...