A Parameter-free Clustering Algorithm based K-means

Slaoui, Saïd Chah; Dafir, Zineb

doi:10.14569/ijacsa.2021.0120372

Cited by 1 publication

(2 citation statements)

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“…The PFK-means algorithm [21] is a parameter-free clustering algorithm aiming to construct progressively homogeneous clusters until the appropriate number of clusters is automatically detected. This heuristic is a combination of the E-transitive heuristic [22] adjusted for quantitative data, and the traditional K-means [10] [11].…”

Section: A the Sequential Pfk-meansmentioning

confidence: 99%

“…Thus, it exposes a general presentation of the parallel computing platforms and the main process of the Spark framework. Moreover, this paper presents a preview of the sequential version of the PFK-means algorithm [21]as well as a detailed explanation of the proposed parallel algorithm. Furthermore, experiments based on UCI data sets were conducted based on the sum of squared errors and the execution time.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

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An Efficient Parallel Algorithm for Clustering Big Data based on the Spark Framework

Dafir¹,

Slaoui²

2022

IJACSA

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The principal objective of this paper is to provide a parallel implementation focused on the main steps of the parameter-free clustering algorithm based on K-means (PFKmeans) using the Spark framework and a machine learningbased model to process Big Data. Thus, the process consists of parallelizing the main tasks of the first stage of the PFK-means clustering algorithm using successive RDD functions. Then, the parallel K-means provided by Spark MLlib is invoked by setting the cluster centers and the number of clusters determined in the previous step as input parameters of the parallel Kmeans. Furthermore, a comparison between the parallel designed algorithm and the parallel K-means was conducted using UCI data sets in terms of the sum of squared errors and the processing time. The experimental results, performed locally using the Spark framework, demonstrate the efficiency of the proposed solution.

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Section: A the Sequential Pfk-meansmentioning

confidence: 99%

Section: Conclusion and Perspectivementioning

confidence: 99%