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R implementation of robust continuous clustering

Authors: Wulff, Dirk;

R implementation of robust continuous clustering

Abstract

R wrapper for a Python implementation of robust continuous clustering (RCC) created by Yann Henon (https://github.com/yhenon/pyrcc). RCC is a powerful clustering algorithm that achieves high accuracy across domains, can handle high data dimensionality, and scales well to large datasets. For details, see the original publication by Shah and Koltun (2017, https://www.pnas.org/doi/10.1073/pnas.1700770114).

Keywords

R, clustering

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average