A survey on parallel clustering algorithms for Big Data
Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithm...
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| Published in: | The Artificial intelligence review Vol. 54; no. 4; pp. 2411 - 2443 |
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| Format: | Journal Article |
| Language: | English |
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01.04.2021
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| ISSN: | 0269-2821, 1573-7462 |
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| Abstract | Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithms. These traditional clustering algorithms present some serious issues in connection with the speed-up, the throughput, and the scalability. Thus, they can no longer be directly used in the context of Big Data, where data are mainly characterized by their volume, velocity, and variety. In order to overcome their limitations, the research today is heading to the parallel computing concept by giving rise to the so-called parallel clustering algorithms. This paper presents an overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms. The former category includes peer-to-peer networks, MapReduce, and Spark platforms, while the latter category includes Multi-core processors, Graphics Processing Unit, and Field Programmable Gate Arrays platforms. In addition, it includes a comparison of the performance of the reviewed algorithms based on some common criteria of clustering validation in the Big Data context. Therefore, it provides the reader with an overall vision of the current parallel clustering techniques. |
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| AbstractList | Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithms. These traditional clustering algorithms present some serious issues in connection with the speed-up, the throughput, and the scalability. Thus, they can no longer be directly used in the context of Big Data, where data are mainly characterized by their volume, velocity, and variety. In order to overcome their limitations, the research today is heading to the parallel computing concept by giving rise to the so-called parallel clustering algorithms. This paper presents an overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms. The former category includes peer-to-peer networks, MapReduce, and Spark platforms, while the latter category includes Multi-core processors, Graphics Processing Unit, and Field Programmable Gate Arrays platforms. In addition, it includes a comparison of the performance of the reviewed algorithms based on some common criteria of clustering validation in the Big Data context. Therefore, it provides the reader with an overall vision of the current parallel clustering techniques. |
| Audience | Academic |
| Author | Lamari, Yasmine Slaoui, Said Chah Dafir, Zineb |
| Author_xml | – sequence: 1 givenname: Zineb orcidid: 0000-0002-9438-3262 surname: Dafir fullname: Dafir, Zineb email: zineb.dafir@um5s.net.ma organization: Faculty of Science of Rabat, Mohammed V University – sequence: 2 givenname: Yasmine surname: Lamari fullname: Lamari, Yasmine organization: Faculty of Science of Rabat, Mohammed V University – sequence: 3 givenname: Said Chah surname: Slaoui fullname: Slaoui, Said Chah organization: Faculty of Science of Rabat, Mohammed V University |
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| Keywords | means Algorithms FPGA MPI Big Data DBSCAN Spark Clustering Data mining GPU Multi-cores CPU MapReduce |
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