A new scalable distributed k-means algorithm based on Cloud micro-services for High-performance computing

Benchara, Fatéma Zahra; Youssfi, Mohamed

doi:10.1016/j.parco.2020.102736

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…A distributed k-means method has proposed by Benchara and Youssfi [59]. It integrates a parallel virtual distributed computing model with a low-cost communication mechanism.…”

Section: Distributed K-meansmentioning

confidence: 99%

From distributed machine to distributed deep learning: a comprehensive survey

Dehghani,

Yazdanparast

2023

J Big Data

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Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be time-consuming and require a great deal of computation. To address these issues, distributed machine learning has been proposed, which involves distributing the data and algorithm across several machines. There has been considerable effort put into developing distributed machine learning algorithms, and different methods have been proposed so far. We divide these algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of the studies have focused on this approach. Therefore, we mostly concentrate on this category. Based on the investigation of the mentioned algorithms, we highlighted the limitations that should be addressed in future research.

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“…A distributed k-means method has proposed by Benchara and Youssfi [59]. It integrates a parallel virtual distributed computing model with a low-cost communication mechanism.…”

Section: Distributed K-meansmentioning

confidence: 99%

From distributed machine to distributed deep learning: a comprehensive survey

Dehghani,

Yazdanparast

2023

J Big Data

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“…The authors in [7] have demonstrated a parallel and distributed framework to increase data scalability using k-mean algorithm in big data environment. The authors used a protocol named AMQP to apply asynchronous communication mechanism.…”

Section: Speed Improvement Of K-means Algorithmmentioning

confidence: 99%

A Survey on Unsupervised K-Means Algorithm in Big Data Environment

Al-deen

Ba-Alwi

2021

AJRCoS

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Due to the rapid development in information technology, Big Data has become one of its prominent feature that had a great impact on other technologies dealing with data such as machine learning technologies. K-mean is one of the most important machine learning algorithms. The algorithm was first developed as a clustering technology dealing with relational databases. However, the advent of Big Data has highly effected its performance. Therefore, many researchers have proposed several approaches to improve K-mean accuracy in Big Data environment. In this paper, we introduce a literature review about different technologies proposed for k-mean algorithm development in Big Data. We demonstrate a comparison between them according to several criteria, including the proposed algorithm, the database used, Big Data tools, and k-mean applications. This paper helps researchers to see the most important challenges and trends of the k-mean algorithm in the Big Data environment.

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