Efficient and Reliable Clustering by Parallel Random Swap Algorithm

Solving large-scale clustering problems requires an efficient algorithm which can be implemented also 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 random swap clustering algorithm. It combines th...

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Bibliographic Details
Published in:2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT) pp. 25 - 28
Main Authors: Nigro, Libero, Cicirelli, Franco, Franti, Pasi
Format: Conference Proceeding
Language:English
Published: IEEE 26.09.2022
Subjects:
Benchmark testing
Clustering algorithms
Clustering methods
Clustering problem
Java
K-Means
Lambda Expressions
Parallelism
Random swap
Real-time systems
Scalability
Sociology
Streams
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Summary:Solving large-scale clustering problems requires an efficient algorithm which can be implemented also 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 random swap clustering algorithm. It combines the scalability of k-means with high clustering accuracy. The new clustering method is experimented on top of Java parallel streams and lambda expressions, which offer interesting execution time benefits. The method is 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 by parallel random swap together with a high time efficiency.
DOI:10.1109/DS-RT55542.2022.9932090