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casa 22(1): e5

Research Article

Automatic Data Clustering using Dynamic Crow Search Algorithm

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  • @ARTICLE{10.4108/eai.17-5-2022.173982,
        author={Rajesh Ranjan and Jitender Kumar Chhabra},
        title={Automatic Data Clustering using Dynamic Crow Search Algorithm},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={8},
        number={1},
        publisher={EAI},
        journal_a={CASA},
        year={2022},
        month={5},
        keywords={CVNN, Data Clustering, Meta-heuristic Search Algorithm},
        doi={10.4108/eai.17-5-2022.173982}
    }
    
  • Rajesh Ranjan
    Jitender Kumar Chhabra
    Year: 2022
    Automatic Data Clustering using Dynamic Crow Search Algorithm
    CASA
    EAI
    DOI: 10.4108/eai.17-5-2022.173982
Rajesh Ranjan1,*, Jitender Kumar Chhabra1
  • 1: National Institute of Technology Kurukshetra
*Contact email: iiitm.rajesh@gmail.com

Abstract

This work proposes Automatic clustering using Dynamic Crow Search Algorithm, which updates its parameters dynamically. Crow Search is a recently proposed algorithm that imitates the working of crow. Clustering is an essential aspect of data analysis whose significance has increased manifold since the advancements of technology which has led to enormous data generation, which need to be analysed in real-time. Automatic clustering detects optimal cluster numbers and produces sustainable cluster centroids. ACDCSA uses Cluster Validity using Nearest Neighbour as an internal validity measure that acts as a fitness function to find the optimal cluster centres. The present work is compared with some well-known other meta-heuristic search algorithms like PSO, DE, WOA and GWO for the automatic clustering task over seven benchmark clustering datasets. Inter-cluster distance, intra-cluster distance and the optimal cluster number produced are used to assess the performance of ACDCSA.

Keywords
CVNN, Data Clustering, Meta-heuristic Search Algorithm
Received
2022-03-12
Accepted
2022-05-13
Published
2022-05-17
Publisher
EAI
http://dx.doi.org/10.4108/eai.17-5-2022.173982

Copyright © 2022 Rajesh Ranjan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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