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• W2990457403 abstract "This article presents an automatic k-means clustering solution targeting the Sunway TaihuLight supercomputer. We first introduce a multilevel parallel partition approach that not only partitions by dataflow and centroid, but also by dimension, which unlocks the potential of the hierarchical parallelism in the heterogeneous many-core processor and the system architecture of the supercomputer. The parallel design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability. Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering tasks with a set of candidate hyper-parameter, and then determining the optimal hyper-parameter using a proposed evaluation method. The proposed autoclustering solution can not only achieve high performance and scalability for problems with massive high-dimensional data, but also support clustering without sufficient prior knowledge for the number of targeted clusters, which can potentially increase the scope of k-means algorithm to new application areas." @default.
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• W2990457403 date "2020-05-01" @default.
• W2990457403 modified "2023-10-02" @default.
• W2990457403 title "Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer" @default.
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• W2990457403 doi "https://doi.org/10.1109/tpds.2019.2955467" @default.
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