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Parallel random swap: An efficient and reliable clustering algorithm in Java.

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Název: Parallel random swap: An efficient and reliable clustering algorithm in Java.
Autoři: Nigro, Libero1 (AUTHOR) libero.nigro@unical.it, Cicirelli, Franco2 (AUTHOR), Fränti, Pasi3 (AUTHOR)
Zdroj: Simulation Modelling Practice & Theory. Apr2023, Vol. 124, pN.PAG-N.PAG. 1p.
Témata: *PARALLEL algorithms, *ALGORITHMS, *K-means clustering, *SCALABILITY
Abstrakt: • Original parallel support in Java of the random swap clustering algorithm. • Applying the tool to 15 benchmark datasets, with two very challenging to solve. • Demonstrating accurate clustering and significant execution speedups. Solving large-scale clustering problems requires an efficient algorithm that can also be implemented in parallel. K-means would be suitable, but it can lead to an inaccurate clustering result. To overcome this problem, we present a parallel version of the random swap clustering algorithm. It combines the scalability of k-means with the high clustering accuracy of random swap. The algorithm is implemented in Java in two ways. The first implementation uses Java parallel streams and lambda expressions. The solution exploits a built-in multi-threaded organization capable of offering competitive speedup. The second implementation is achieved on top of the Theatre actor system which ensures better scalability and high-performance computing through fine-grain resource control. The two implementations are then applied to standard benchmark datasets, with a varying population size and distribution of managed records, dimensionality of data points and the number of clusters. The experimental results confirm that high-quality clustering can be obtained together with a very good execution efficiency. Our Java code is publicly available at: https://github.com/uef-machine-learning. [ABSTRACT FROM AUTHOR]
Databáze: Academic Search Index
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