High-Performance Hybrid Algorithm for Minimum Sum-of-Squares Clustering of Infinitely Tall Data
Mussabayev R. Mussabayev R.
July 2024Multidisciplinary Digital Publishing Institute (MDPI)
Mathematics
2024#12Issue 13
This paper introduces a novel formulation of the clustering problem, namely, the minimum sum-of-squares clustering of infinitely tall data (MSSC-ITD), and presents HPClust, an innovative set of hybrid parallel approaches for its effective solution. By utilizing modern high-performance computing techniques, HPClust enhances key clustering metrics: effectiveness, computational efficiency, and scalability. In contrast to vanilla data parallelism, which only accelerates processing time through the MapReduce framework, our approach unlocks superior performance by leveraging the multi-strategy competitive–cooperative parallelism and intricate properties of the objective function landscape. Unlike other available algorithms that struggle to scale, our algorithm is inherently parallel in nature, improving solution quality through increased scalability and parallelism and outperforming even advanced algorithms designed for small- and medium-sized datasets. Our evaluation of HPClust, featuring four parallel strategies, demonstrates its superiority over traditional and cutting-edge methods by offering better performance in the key metrics. These results also show that parallel processing not only enhances the clustering efficiency, but the accuracy as well. Additionally, we explore the balance between computational efficiency and clustering quality, providing insights into optimal parallel strategies based on dataset specifics and resource availability. This research advances our understanding of parallelism in clustering algorithms, demonstrating that a judicious hybridization of advanced parallel approaches yields optimal results for MSSC-ITD. Experiments on the synthetic data further confirm HPClust’s exceptional scalability and robustness to noise.
adaptive algorithm , big data , clustering , data sampling , decomposition , global optimization , high-performance computing , HPClust algorithm , hybrid approach , K-means , K-means++ , large-scale datasets , minimum sum-of-squares , multi-strategy optimization , parallel processing
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Department of Mathematics, University of Washington, Padelford Hall C-138, Seattle, 98195-4350, WA, United States
AI Research Lab, Satbayev University, Satbaev Str. 22, Almaty, 050013, Kazakhstan
Laboratory for Analysis and Modeling of Information Processes, Institute of Information and Computational Technologies, Pushkin Str. 125, Almaty, 050010, Kazakhstan
Department of Mathematics
AI Research Lab
Laboratory for Analysis and Modeling of Information Processes
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