2022
DOI: 10.1002/ett.4695
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An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Abstract: Ensemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an en… Show more

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“…MapReduce's applications, leveraging its features and benefits [70], span data mining and extraction for reports [71], big-data graphical computation [72], machine learning challenges [73], statistical machine translation [74], spam detection [75] satellite image data processing [76], and problem clustering [77], among others. MapReduce operates through a combination of map and reduce functions, which together handle machine failures, parallelize computations across vast clusters, and facilitate inter-machine communication scheduling [78].…”
Section: Mapreducementioning
confidence: 99%
“…MapReduce's applications, leveraging its features and benefits [70], span data mining and extraction for reports [71], big-data graphical computation [72], machine learning challenges [73], statistical machine translation [74], spam detection [75] satellite image data processing [76], and problem clustering [77], among others. MapReduce operates through a combination of map and reduce functions, which together handle machine failures, parallelize computations across vast clusters, and facilitate inter-machine communication scheduling [78].…”
Section: Mapreducementioning
confidence: 99%