Efficient algorithm for big data clustering on single machine
Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k-means algorithm. It significantly...
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| Published in: | CAAI Transactions on Intelligence Technology Vol. 5; no. 1; pp. 9 - 14 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Beijing
The Institution of Engineering and Technology
01.03.2020
John Wiley & Sons, Inc Wiley |
| Subjects: | |
| ISSN: | 2468-2322, 2468-6557, 2468-2322 |
| Online Access: | Get full text |
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| Abstract | Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth of computations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initial dataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for each batch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real large datasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposed approach is compared with k-means and its modification. The experiments show that the proposed algorithm is a promising tool for clustering large datasets in comparison with the k-means algorithm. |
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| AbstractList | Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k‐means algorithm. It significantly reduces the exponential growth of computations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initial dataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for each batch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real large datasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposed approach is compared with k‐means and its modification. The experiments show that the proposed algorithm is a promising tool for clustering large datasets in comparison with the k‐means algorithm. |
| Author | Sukhostat, Lyudmila V Aliguliyev, Ramiz M Alguliyev, Rasim M |
| Author_xml | – sequence: 1 givenname: Rasim M surname: Alguliyev fullname: Alguliyev, Rasim M organization: Institute of Information Technology, Azerbaijan National Academy of Sciences, 9A, B. Vahabzade Street, AZ1141 Baku, Azerbaijan – sequence: 2 givenname: Ramiz M orcidid: 0000-0001-9795-1694 surname: Aliguliyev fullname: Aliguliyev, Ramiz M email: r.aliguliyev@gmail.com organization: Institute of Information Technology, Azerbaijan National Academy of Sciences, 9A, B. Vahabzade Street, AZ1141 Baku, Azerbaijan – sequence: 3 givenname: Lyudmila V orcidid: 0000-0001-9449-7457 surname: Sukhostat fullname: Sukhostat, Lyudmila V organization: Institute of Information Technology, Azerbaijan National Academy of Sciences, 9A, B. Vahabzade Street, AZ1141 Baku, Azerbaijan |
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| Copyright | 2020 CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of Chongqing University of Technology 2020. This work is published under http://creativecommons.org/licenses/by-nc-nd/3.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | data analysis computing powers single machine big data clustering data processing Big Data data points cluster centroids initial dataset pattern clustering clustering algorithms clustering speed k-means algorithm big data analysis |
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| Title | Efficient algorithm for big data clustering on single machine |
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