Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor
Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been im...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/6/883 |
| _version_ | 1797471903151030272 |
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| author | Fouad H. Awad Murtadha M. Hamad |
| author_facet | Fouad H. Awad Murtadha M. Hamad |
| author_sort | Fouad H. Awad |
| collection | DOAJ |
| description | Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means. |
| first_indexed | 2024-03-09T19:54:40Z |
| format | Article |
| id | doaj.art-ff31302813794dc5be0f3cddde1cf9f2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T19:54:40Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ff31302813794dc5be0f3cddde1cf9f22023-11-24T01:00:50ZengMDPI AGElectronics2079-92922022-03-0111688310.3390/electronics11060883Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine ProcessorFouad H. Awad0Murtadha M. Hamad1College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, IraqCollege of Computer Science and Information Technology, University of Anbar, Ramadi 31001, IraqClustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means.https://www.mdpi.com/2079-9292/11/6/883big dataclusteringneural enginek-meansparallel computing |
| spellingShingle | Fouad H. Awad Murtadha M. Hamad Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor Electronics big data clustering neural engine k-means parallel computing |
| title | Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor |
| title_full | Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor |
| title_fullStr | Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor |
| title_full_unstemmed | Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor |
| title_short | Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor |
| title_sort | improved k means clustering algorithm for big data based on distributed smartphoneneural engine processor |
| topic | big data clustering neural engine k-means parallel computing |
| url | https://www.mdpi.com/2079-9292/11/6/883 |
| work_keys_str_mv | AT fouadhawad improvedkmeansclusteringalgorithmforbigdatabasedondistributedsmartphoneneuralengineprocessor AT murtadhamhamad improvedkmeansclusteringalgorithmforbigdatabasedondistributedsmartphoneneuralengineprocessor |