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Scalable Clustering Algorithms for Big data: A Review
Faculty
Computer Science
Year:
2021
Type of Publication:
ZU Hosted
Pages:
80015 - 80027
Authors:
Khalied Mohamed Hosny
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE Access IEEE
Volume:
Keywords :
Scalable Clustering Algorithms , , data: , Review
Abstract:
Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.
Author Related Publications
Khalied Mohamed Hosny, "SEMANTIC REPRESENTATION OF MUSIC DATABASE USING NEW ONTOLOGY-BASED SYSTEM", Journal of Theoretical and Applied Information Technology, 2020
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Khalied Mohamed Hosny, "Building a New Semantic Social Network Using Semantic Web-Based Techniques", ِASPG, 2021
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Khalied Mohamed Hosny, "New Graphical Ultimate Processor for Mapping Relational Database to Resource Description Framework", IEEE, 2022
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Khalied Mohamed Hosny, "Fast computation of accurate Zernike moments", Springer, 2008
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Khalied Mohamed Hosny, "Accurate Computation of QPCET for Color Images in Different Coordinate Systems", SPIE, 2017
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Department Related Publications
Ahmed Salah Mohamed Mostafa, "Cluster-Distribute-Align-Merge: A General Algorithm to Speed Up Multiple Sequence Alignment on Multi-Core Computers", Journal of Computational and Theoretical Nanoscience, 2014
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Zaher Awad Aboelenieen Elhendy, "NEW APPROACH TO IMAGE EDGE DETECTION BASED ON QUANTUM ENTROPY", JOURNAL OF RUSSIAN LASER RESEARCH, 2016
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Sarah AbdelRazek Ahmed AbdulHameid, "Cloud Storage Forensics: Survey", International Journal of Engineering Trends and Technology (IJETT), 2017
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Doaa El-Shahat Barakat Mohammed, "A modified hybrid whale optimization algorithm for the scheduling problem in multimedia data objects", Wiley online library, 2019
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Mohammed Abdel Basset Metwally Attia, "A novel model for evaluation Hospital medical care systems based on plithogenic sets", Elsevier B.V., 2019
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