Implementing Parallel Computing to Enhance the Performance of K-mean Algorithm

Taha, Ranyah; Alshakrani, Sara; Alqaddoumi, Abdallah

doi:10.1109/icdabi53623.2021.9655811

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Its efficiency of a program in a concurrent environment was enhanced by adding a K-Means clustering algorithm to an MPI4py module. This report examines the effectiveness of executing the K-Means method consecutively versus using a Message Passing Interface (MPI) parallel design for grouping information in the form of operational costs and processing time [19]. This study presented a parallelization K-Means method for sparse, elevated text (PKHT).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Baligodugula,

Amsaad,

Schmidt

et al. 2024

Preprint

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Unsupervised ML-based approaches have emerged for driving critical decisions about training data samples to help solve challenges in many life critical applications. This paper proposes parallel and distributed computing unsupervised ML techniques to improve the execution time of different ML algorithms. Various unsupervised ML models are developed, implemented, and tested to demonstrate the efficiency, in terms of execution time and accuracy, of the serial methods as compared to the parallelized ones. We developed sequential, parallel, and distributed cloud computing unsupervised ML models based and determined the most efficient model through comparative analysis. As a case study, sequential, parallel, and distributed approaches of Simple K-Means, Minibatch K-means, and Fuzzy C-Means are investigated to study the developed models’ efficiency using country datasets for multiple organizations to train and test the developed model. Parallel and distributed computing models are developed utilizing could computing architect, i.e., cloud Amazon SageMaker, to study their efficiency in the execution time and model accuracy. The results show that the proposed parallel and distributed Fuzzy C-Means outperforms the other two clustering methods in terms of execution time with 0.932ms and 0.623ms with a minimal impact on the accuracy of the developed models.

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Section: Related Workmentioning

confidence: 99%

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Baligodugula,

Amsaad,

Schmidt

et al. 2024

Preprint

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show abstract

“…e min-max similarity measure is used to calculate the distance. (0, 1) e min-max similarity measure [15] is used to calculate the distance. e K-Mean algorithm, developed by Singh and Bhatia [16], identi es items with the lowest frequency.…”

Section: Simple Andmentioning

confidence: 99%

Performance Evaluation of Simple K-Mean and Parallel K-Mean Clustering Algorithms: Big Data Business Process Management Concept

Zada

Ali

Khan³

et al. 2022

Mobile Information Systems

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Data is the most valuable asset in any firm. As time passes, the data expands at a breakneck speed. A major research issue is the extraction of meaningful information from a complex and huge data source. Clustering is one of the data extraction methods. The basic K-Mean and Parallel K-Mean partition clustering algorithms work by picking random starting centroids. The basic and K-Mean parallel clustering methods are investigated in this work using two different datasets with sizes of 10000 and 5000, respectively. The findings of the Simple K-Mean clustering algorithms alter throughout numerous runs or iterations, according to the study, and so iterations differ for each run or execution. In some circumstances, the clustering algorithms’ outcomes are always different, and the algorithms separate and identify unique properties of the K-Mean Simple clustering algorithm from the K-Mean Parallel clustering algorithm. Differentiating these features will improve cluster quality, lapsed time, and iterations. Experiments are designed to show that parallel algorithms considerably improve the Simple K-Mean techniques. The findings of the parallel techniques are also consistent; however, the Simple K-Mean algorithm’s results vary from run to run. Both the 10,000 and 5000 data item datasets are divided into ten subdatasets for ten different client systems. Clusters are generated in two iterations, i.e., the time it takes for all client systems to complete one iteration (mentioned in chapter number 4). In the first execution, Client No. 5 has the longest elapsed time (8 ms), whereas the longest elapsed time in the following iterations is 6 ms, for a total elapsed time of 12 ms for the K-Mean clustering technique. In addition, the Parallel algorithms reduce the number of executions and the time it takes to complete a task.

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Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques

Rosli

Setumin

Nugroho

et al. 2022

2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA)

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Implementing Parallel Computing to Enhance the Performance of K-mean Algorithm

Cited by 3 publications

References 8 publications

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Performance Evaluation of Simple K-Mean and Parallel K-Mean Clustering Algorithms: Big Data Business Process Management Concept

Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques

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