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

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...

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Veröffentlicht in:Mobile information systems Jg. 2022; S. 1 - 15
Hauptverfasser: Zada, Islam, Ali, Shaukat, Khan, Inayat, Hadjouni, Myriam, Elmannai, Hela, Zeeshan, Muhammad, Serat, Ali Mohammad, Jameel, Abid
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Hindawi 23.06.2022
John Wiley & Sons, Inc
Schlagworte:
Academic achievement
Algorithms
Big Data
Business process management
Centroids
Clustering
Data mining
Datasets
Employees
Learning
Performance evaluation
Research methodology
Similarity measures
Students
ISSN:1574-017X, 1875-905X
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Zusammenfassung: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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ISSN:1574-017X
1875-905X
DOI:10.1155/2022/1277765