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- W3006235004 abstract "Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing. We prove that this method yields optimal clustering assignments under a separability assumption, and show that the generated clustering assignments are of comparable quality to those of assignments generated by common clustering algorithms, yet can be obtained a full order of magnitude faster." @default.
- W3006235004 created "2020-02-24" @default.
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- W3006235004 date "2020-01-01" @default.
- W3006235004 modified "2023-09-23" @default.
- W3006235004 title "A Quantum Annealing-Based Approach to Extreme Clustering" @default.
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- W3006235004 doi "https://doi.org/10.1007/978-3-030-39442-4_15" @default.
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