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Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework

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Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1038))

Abstract

Clustering large data is a recent and popular challenge that is used in various applications, including social networking, bioinformatics, and many others. In order to manage the rapidly growing data sizes, traditional clustering algorithms must be improved. In this research, a hybrid Harris Hawks Optimizer (HHHO) with K-mean clustering and MapReduce framework is proposed to solve the various data clustering problem. The proposed scheme uses the K-means' ability to solve the various clustering problems. More specifically, the K-means are utilized as initial solutions to the traditional Harris Hawks Optimizer (HHO). In general, HHO tries to refine the candidate solutions to find the best one. MapReduce is a distributed processing computing paradigm that produces datasets using a parallel program on a cluster. In particular, it is adopted in the developed HHHO for parallelization since it offers fault tolerance, load balancing, and data locality. The performance of the presented methodology has been evaluated by means of numerical comparisons which proved the efficiency of the proposed HHHO, where it produces better results than other existing computation methods. Moreover, it has a very good ability in improving and finding optimal and converging sets of data. In addition, the accuracy and error rate of the obtained results are assessed. The proposed method is implemented and evaluated using PYTHON simulation settings.

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Author information

Authors and Affiliations

  1. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Mohammad Qassem Bashabsheh, Laith Abualigah & Mohammad Alshinwan

  2. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

Authors
  1. Mohammad Qassem Bashabsheh
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  2. Laith Abualigah
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  3. Mohammad Alshinwan
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Corresponding author

Correspondence to Laith Abualigah .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

  2. Faculty of Computer Science & Engineering, Galala University, Suze, Egypt

    Mohamed Abd Elaziz

  3. Department of Computer Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

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Bashabsheh, M.Q., Abualigah, L., Alshinwan, M. (2022). Big Data Analysis Using Hybrid Meta-Heuristic Optimization Algorithm and MapReduce Framework. In: Houssein, E.H., Abd Elaziz, M., Oliva, D., Abualigah, L. (eds) Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Studies in Computational Intelligence, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-99079-4_8

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