Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU

ISMI, DEWI PRAMUDI and ARDIANTO, FAHRI Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU. [Artikel Dosen]

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Abstract

K-medoids clustering is categorized as partitional clustering. K-medoids
offers better result when dealing with outliers and arbitrary distance metric
also in the situation when the mean or median does not exist within data.
However, k-medoids suffers a high computational complexity. Partitioning
Around Medoids (PAM) has been developed to improve k-medoids
clustering, consists of build and swap steps and uses the entire dataset to find
the best potential medoids. Thus, PAM produces better medoids than other
algorithms. This research proposes the parallelization of PAM in k-medoids
clustering on GPU to reduce computational time at the swap step of PAM.
The parallelization scheme utilizes shared memory, reduction algorithm, and
optimization of the thread block configuration to maximize the occupancy.
Based on the experiment result, the proposed parallelized PAM k-medoids is
faster than CPU and Matlab implementation and efficient for large dataset.

Item Type:	Artikel Dosen
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	Dewi Pramudi Ismi
Date Deposited:	07 Oct 2020 07:02
Last Modified:	07 Oct 2020 07:02
URI:	http://eprints.uad.ac.id/id/eprint/20801

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