Large Dataset Classification Using Parallel Processing Concept
Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number o...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Department of Information Technology - Politeknik Negeri Padang
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30480/1/Large%20Dataset%20Classification.pdf |
| _version_ | 1825813691400978432 |
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| author | Aljanabi, Mohammad Ebraheem, Hind Ra'ad Hussain, Zahraa Faiz Mohd Farhan, Md Fudzee Shahreen, Kasim Mohd Arfian, Ismail Meidelfie, Dwiny Eriandae, Aldo |
| author_facet | Aljanabi, Mohammad Ebraheem, Hind Ra'ad Hussain, Zahraa Faiz Mohd Farhan, Md Fudzee Shahreen, Kasim Mohd Arfian, Ismail Meidelfie, Dwiny Eriandae, Aldo |
| author_sort | Aljanabi, Mohammad |
| collection | UMP |
| description | Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier. |
| first_indexed | 2024-03-06T12:47:46Z |
| format | Article |
| id | UMPir30480 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:47:46Z |
| publishDate | 2020 |
| publisher | Department of Information Technology - Politeknik Negeri Padang |
| record_format | dspace |
| spelling | UMPir304802021-01-12T07:36:30Z http://umpir.ump.edu.my/id/eprint/30480/ Large Dataset Classification Using Parallel Processing Concept Aljanabi, Mohammad Ebraheem, Hind Ra'ad Hussain, Zahraa Faiz Mohd Farhan, Md Fudzee Shahreen, Kasim Mohd Arfian, Ismail Meidelfie, Dwiny Eriandae, Aldo QA Mathematics QA75 Electronic computers. Computer science Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier. Department of Information Technology - Politeknik Negeri Padang 2020 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/30480/1/Large%20Dataset%20Classification.pdf Aljanabi, Mohammad and Ebraheem, Hind Ra'ad and Hussain, Zahraa Faiz and Mohd Farhan, Md Fudzee and Shahreen, Kasim and Mohd Arfian, Ismail and Meidelfie, Dwiny and Eriandae, Aldo (2020) Large Dataset Classification Using Parallel Processing Concept. JOIV: International Journal on Informatics Visualization, 4 (4). pp. 191-194. ISSN 2549-9904. (Published) http://dx.doi.org/10.30630/joiv.4.4.361 http://dx.doi.org/10.30630/joiv.4.4.361 |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science Aljanabi, Mohammad Ebraheem, Hind Ra'ad Hussain, Zahraa Faiz Mohd Farhan, Md Fudzee Shahreen, Kasim Mohd Arfian, Ismail Meidelfie, Dwiny Eriandae, Aldo Large Dataset Classification Using Parallel Processing Concept |
| title | Large Dataset Classification Using Parallel Processing Concept |
| title_full | Large Dataset Classification Using Parallel Processing Concept |
| title_fullStr | Large Dataset Classification Using Parallel Processing Concept |
| title_full_unstemmed | Large Dataset Classification Using Parallel Processing Concept |
| title_short | Large Dataset Classification Using Parallel Processing Concept |
| title_sort | large dataset classification using parallel processing concept |
| topic | QA Mathematics QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/30480/1/Large%20Dataset%20Classification.pdf |
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