An efficient meta-heuristic algorithm based on water flow optimizer for data clustering (2024)

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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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An efficient meta-heuristic algorithm based on water flow optimizer for data clustering (1)

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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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      An efficient meta-heuristic algorithm based on water flow optimizer for data clustering (87)

      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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              FAQs

              What is an example of a meta heuristic? ›

              Common examples of metaheuristic algorithms include: Genetic algorithms (GA), Particle swarm optimization (PSO), Simulated annealing (SA), Tabu search (TS), differential evolution (DE) algorithm, and swarm intelligence algorithms.

              What is the metaheuristic algorithm? ›

              In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with ...

              What is the difference between heuristic and meta heuristic? ›

              A heuristic is a technique aimed to solve a problem faster when traditional techniques are too slow. A metaheuristic is a higher-level technique or heuristic that seeks, generates, or selects a heuristic that may provide a sufficiently good solution to an optimization problem (Attea et al.

              What are the advantages and disadvantages of metaheuristic algorithms? ›

              Advantages: Metaheuristic algorithms provide consistent solutions for optimizing neural network training parameters. Disadvantages: The traditional heuristic approach may have poorer convergence speed and may end up with local optima.

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              Examples of heuristics in real-world applications include: - Metaheuristic algorithms applied to enhance the performance of intelligent systems in various domains such as data clustering, wireless sensor networks, traffic light control systems, deep neural networks, and convolutional neural networks .

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              The availability heuristic can influence our perception of risk in everyday life. One common example occurs when we are considering buying insurance. The sharp increase in purchases of flood insurance in the aftermath of flood events illustrates this phenomenon.

              What are metaheuristic algorithms for clustering? ›

              The metaheuristic algorithms applied to solve clustering problems include the tabu search and the simulated annealing algorithms as well as evolutionary algorithms like the genetic algorithm, the artificial bee colony optimization, the particle swarm optimization, and the ant colony optimization algorithms.

              What are the best metaheuristic algorithms? ›

              The result of the TOPSIS method indicates the superiority and efficiency of the tabu search algorithm. However, the analytical hierarchy process presents the ant colony algorithm as the best algorithm. Also, in the AHP-TOPSIS method, the best meta-heuristic algorithm is genetic.

              What are heuristic optimization algorithms? ›

              Heuristic optimization algorithms are artificial intelligence search methods that can be used to find the optimal decisions for designing or managing a wide range of complex systems.

              Why do we use meta heuristics? ›

              An optimum metaheuristics solution is preferable for NP-hard problems in which computing time increases exponentially with problem size. For complex problems and those with large instances, metaheuristics is better at finding an optimum solution with less time complexity.

              What is a meta heuristic approach in AI? ›

              Meta-heuristics are generic methods that may be used to solve complex and chal- lenging combinatorial search problems. These problems are challenging for com- puter scientists because solving them involves examining a huge number – usu- ally exponential – of combinations.

              Are heuristics better than algorithms? ›

              They are exhaustive and guarantee the correct solution, but may be time-consuming and require a lot of mental effort. In contrast, heuristics are shortcut strategies or rules-of-thumb. Although they may save time and effort, they are not always guaranteed to yield the correct solution.

              What is the drawback of heuristic algorithm? ›

              There are also drawbacks to using heuristics. While they may be quick and dirty, they will likely not produce the optimal decision and can also be wrong entirely. Quick decisions without all the information can lead to errors in judgment, and miscalculations can lead to mistakes.

              What is an example of a metaheuristic algorithm? ›

              Famous examples of metaheuristics are genetic algorithms, particle swarm optimization, simulated annealing, and variable neighborhood search.

              What is an advantage of a heuristic algorithm? ›

              The main advantage of adopting a heuristic approach is that it offers a quick solution, which is easy to understand and implement. Heuristic algorithms are practical, serving as fast and feasible short-term solutions to planning and scheduling problems.

              What are heuristics and give an example? ›

              Heuristics allow people to go beyond their cognitive limits. Heuristics are also advantageous when speed or timeliness matters—for example, deciding to enter a trade or making a snap judgment about some important decision. Heuristics are thus handy when there is no time to carefully weigh all options and their merits.

              What is an example of the best heuristic? ›

              The task is to infer which of two alternatives has the higher criterion value. An example is which of two NBA teams will win the game, based on cues such as home match and who won the last match.

              What is heuristic method with example? ›

              Heuristic methods are reliable and convenient mental shortcuts that you can use to narrow down your options when you're faced with several different choices, to ease your cognitive load , or to solve problems. Perhaps you're a hiring manager, and you decide to dismiss any résumés that contain spelling mistakes.

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