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.
CITATION STYLE
Jaschek, T., Bucyk, M., & Oberoi, J. S. (2020). A Quantum Annealing-Based Approach to Extreme Clustering. In Advances in Intelligent Systems and Computing (Vol. 1130 AISC, pp. 169–189). Springer. https://doi.org/10.1007/978-3-030-39442-4_15
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