research-article Free Access
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- Ramesh Chandra Sahoo Department of CSE, MRIIRS, Faridabad, India
Department of CSE, MRIIRS, Faridabad, India
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- Tapas Kumar Department of CSE, MRIIRS, Faridabad, India
Department of CSE, MRIIRS, Faridabad, India
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- Poonam Tanwar Department of CSE, MRIIRS, Faridabad, India
Department of CSE, MRIIRS, Faridabad, India
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- Jyoti Pruthi Department of CST, MRU, Faridabad, India
Department of CST, MRU, Faridabad, India
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- Sanjay Singh Department of CST, MRU, Faridabad, India
Department of CST, MRU, Faridabad, India
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The Journal of SupercomputingVolume 80Issue 8May 2024pp 10301–10326https://doi.org/10.1007/s11227-023-05822-y
Published:21 December 2023Publication History
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The Journal of Supercomputing
Volume 80, Issue 8
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Abstract
Abstract
Clustering is a popular data analysis technique that can explore the structure of data through cluster analysis. Similar data are put into the same cluster, while dissimilar data allocate to other clusters. The similarity/dissimilarity among data objects is determined using a distance function. Further, clustering algorithms aim to choose the optimal set of centroids for obtaining better partitioning, but clustering accuracy is always susceptible. This issue of clustering is addressed through meta-heuristic algorithms. This research also aims to handle the accuracy issue and presents a new algorithm for effective cluster analysis. The proposed clustering algorithm is inspired by a water flow optimizer (WFO). The WFO algorithm performance is validated on the well-defined clustering problems based on SSE, accuracy (AR) and detection rate (DR) parameters. The results indicate that the WFO algorithm gets higher clustering results in terms of SSE, AR and DR than the same class of algorithms. The performance is also validated using Friedman statistical test followed by a post hoc test. Results indicated that the proposed WFO gets better statistical results than other clustering algorithms.
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The Journal of Supercomputing Volume 80, Issue 8
May 2024
1648 pages
ISSN:0920-8542
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
- Published: 21 December 2023
- Accepted: 15 November 2023
Author Tags
- Cluster analysis
- Data clustering
- Meta-heuristic algorithm
- Water flow optimizer
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