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- W4311689671 abstract "In unsupervised learning, algorithms are not guided by labels of the training set but they must identify structures into data instances, for example one would like to find preferred directions in the feature space to identify geometric insights or performing a dimensionality reduction to dimension 2 or 3 for providing a graphical representation of the dataset. Without labels, there are not known classes of data a priori but a learning algorithm must be able to divide the data instances into clusters formed by similar data. In this section we present the quantum principal component analysis for dimensionality reduction of the feature space and some clustering techniques obtained embedding quantum subroutines into K-means, K-medians and divise clustering algorithms. Moreover, we discuss the implementation of a 2-means clustering with a quantum annealer." @default.
- W4311689671 created "2022-12-28" @default.
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- W4311689671 date "2023-01-01" @default.
- W4311689671 modified "2023-09-30" @default.
- W4311689671 title "Quantum Clustering" @default.
- W4311689671 doi "https://doi.org/10.1007/978-981-19-6897-6_6" @default.
- W4311689671 hasPublicationYear "2023" @default.
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