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lre

Linier Reference Elbow is a proposed method for determinant clustering

https://github.com/mattbit212/lre

Science Score: 57.0%

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Repository

Linier Reference Elbow is a proposed method for determinant clustering

Basic Info
  • Host: GitHub
  • Owner: mattbit212
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 358 KB
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  • Watchers: 1
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Created 10 months ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

Linear Reference Elbow (LRE)

This repository implements the Linear Reference Elbow (LRE) method for automatically determining the optimal number of clusters in the K-Means algorithm.

Unlike the traditional visual elbow method, LRE uses a geometric approach by computing the orthogonal distance between each WCSS point and a reference line. The method is: - Objective (no manual intervention) - Fast (linear time complexity O(n)) - Reproducible

This implementation is part of the author's research presented in the IOP Conference Series 2025.

📊 Iris dataset

The notebook includes examples using:

- Iris dataset (from sklearn.datasets)

📦 Synthetic Dataset

The file synthetic.xlsx contains 2000 samples divided into 4 clusters: - 2 clusters generated from uniform distributions - Located in opposite quadrants - 2 clusters generated from multivariate normal distributions - One with negative correlation (skewed elliptical shape) - One with unequal variances (vertical ellipse)

This dataset is the same as the one used in the IOP Conference publication. It challenges clustering methods with heterogeneity and variance structure, making it ideal for benchmarking automated methods like LRE.

🚀 How to Use

  1. Clone this repository: ```bash git clone https://github.com/mattbit212/LRE.git cd LRE
  2. Install dependencies (if needed): ```bash pip install -r requirements.txt OR pip install numpy matplotlib scikit-learn
  3. Open and run the notebook: ```bash KMeans_LRE.ipynb
  4. Citation If you use this method or code in your research, please cite the following:

Yusuf, A. (2025). Computational Implementation of Linear Reference Elbow (LRE) Method for Optimal Cluster Determination in K-Means Algorithm. IOP Conference Series. [https://doi.org/10.5281/zenodo.16574800]

Owner

  • Login: mattbit212
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it using the metadata below."
title: "Python Implementation of Linear Reference Elbow (LRE)"
version: "1.0.0"
doi: "10.5281/zenodo.16574800"
date-released: 2025-07-29
authors:
  - family-names: Yusuf
    given-names: Akhmad
    orcid: https://orcid.org/0000-0002-9177-8066
    affiliation: University of Lambung Mangkurat, Indonesia
repository-code: https://github.com/mattbit212/LRE
license: MIT
keywords:
  - clustering
  - k-means
  - elbow method
  - automation
  - LRE

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