pocs-based-clustering 1.3.1

Official Implementation of POCS-based Clustering Algorithm

POCS-based Clustering Algorithm

Official implementation of the Projection Onto Convex Set (POCS)-based clustering algorithm.

Introduction

• Main authors: Le-Anh Tran, Dong-Chul Park

• Abstract:

  This paper proposes a data clustering algorithm that is inspired by the prominent convergence property of the Projection onto Convex Sets (POCS) method, termed the POCS-based clustering algorithm. For disjoint convex sets, the form of simultaneous projections of the POCS method can result in a minimum mean square error solution. Relying on this important property, the proposed POCS-based clustering algorithm treats each data point as a convex set and simultaneously projects the cluster prototypes onto respective member data points, the projections are convexly combined via adaptive weight values in order to minimize a predefined objective function for data clustering purposes. The performance of the proposed POCS-based clustering algorithm has been verified through a large scale of experiments and data sets. The experimental results have shown that the proposed POCS-based algorithm is competitive in terms of both effectiveness and efficiency against some of the prevailing clustering approaches such as the K-means/K-Means++ and Fuzzy C-Means (FCM) algorithms. Based on extensive comparisons and analyses, we can confirm the validity of the proposed POCS-based clustering algorithm for practical purposes.

Usage

Installation

pip install pocs-based-clustering

Function

from pocs_cluster_analysis import pocs_clustering

centroids, labels = pocs_clustering(input_data, num_clusters, num_iterations)

Citation

Please cite our works if you utilize any data from this work for your study.

@inproceedings{tran2022pocs,
  title={POCS-based Clustering Algorithm},
  author={Tran, Le-Anh and Deberneh, Henock M and Do, Truong-Dong and Nguyen, Thanh-Dat and Le, My-Ha and Park, Dong-Chul},
  booktitle={2022 International Workshop on Intelligent Systems (IWIS)},
  pages={1--6},
  year={2022},
  organization={IEEE}
}

@article{tran2024cluster,
  title={Cluster Analysis via Projection onto Convex Sets},
  author={Tran, Le-Anh and Kwon, Daehyun and Deberneh, Henock Mamo and Park, Dong-Chul},
  journal={Intelligent Data Analysis},
  year={2024},
  publisher={IOS Press}
}

Have fun!

LA Tran