Mean-kMeans [ICCIT23]

Overview

This repository contains an implementation of the Mean-kMeans clustering algorithm, an enhanced version of the traditional kMeans algorithm. Our Mean-kMeans algorithm improves clustering performance by optimizing the iteration process and utilizing in-place mean calculations for cluster assignments, thus resulting in faster convergence.

Mean-kMeans Algorithm Details

Mean-kMeans introduces key enhancements:

• Dynamic Mean Calculation: Instead of fixed centroids, the algorithm calculates the mean of points currently assigned to each cluster during each iteration.
• Optimized Convergence: By refining the centroid selection and leveraging in-place calculations, the algorithm significantly reduces the number of iterations needed for convergence.

Installation

To run the code, ensure you have Python installed. You can create a virtual environment for better package management. Use the following commands to set up your environment:

python3 -m venv venv
source venv/bin/activate

And install numpy, matplotlib, scikit-learn, and pandas following:

pip install numpy matplotlib scikit-learn pandas

Usage

1. Clone the repository:

   git clone https://github.com/abdurrahman4127/Mean-kMeans_ICCIT23.git

   Then navigate to the repository directory

2. Run the scripts:

   • To execute the KMeans clustering algorithm:

     python normal_kmeans.py

   • To execute the Mean-kMeans clustering algorithm:

     python mean_kmeans.py

   • To visualize the Elbow method and clustering results, run:

     python elbow_method.py

Notebook is provided at 📙Mean-kMeans

Citation

This study was accepted in 2023 at the 26th International Conference on Computer and Information Technology (ICCIT), held from December 13 to 15 in Cox’s Bazar, Bangladesh. If you find our work useful, consider citing it as:

@INPROCEEDINGS{10441078,
  author={Hasan, Emam and Rahman, Md. Abdur and Shojib Talukder, MD. and Utsho, Md Farnas and Shakhan, Md. and Farid, Dewan Md.},
  booktitle={2023 26th International Conference on Computer and Information Technology (ICCIT)}, 
  title={Data Segmentation with Improved K-Means Clustering Algorithm}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Technological innovation;Clustering methods;Clustering algorithms;Machine learning;Partitioning algorithms;Unsupervised learning;Convergence;Leaning by Observation;Partition-based Clustering;Unsupervised Learning},
  doi={10.1109/ICCIT60459.2023.10441078}}