Data clustering based on Langevin annealing with a self-consistent potential

Lafata, K; Zhou, Z; Liu, JG; Yin, FF

Data clustering based on Langevin annealing with a self-consistent potential

dc.contributor.author	Lafata, K
dc.contributor.author	Zhou, Z
dc.contributor.author	Liu, JG
dc.contributor.author	Yin, FF
dc.date.accessioned	2019-08-20T13:00:32Z
dc.date.available	2019-08-20T13:00:32Z
dc.date.issued	2018-10-11
dc.date.updated	2019-08-20T13:00:27Z
dc.identifier.issn	0033-569X
dc.identifier.issn	1552-4485
dc.identifier.uri	https://hdl.handle.net/10161/19225
dc.language	en
dc.publisher	American Mathematical Society (AMS)
dc.relation.ispartof	Quarterly of Applied Mathematics
dc.relation.isversionof	10.1090/qam/1521
dc.subject	Science & Technology
dc.subject	Physical Sciences
dc.subject	Mathematics, Applied
dc.subject	Mathematics
dc.subject	DIFFUSION MAPS
dc.subject	TIME-SERIES
dc.subject	DYNAMICS
dc.subject	ALGORITHM
dc.subject	GRAPHS
dc.title	Data clustering based on Langevin annealing with a self-consistent potential
dc.type	Journal article
duke.contributor.orcid	Liu, JG\|0000-0002-9911-4045
duke.contributor.orcid	Yin, FF\|0000-0002-2025-4740\|0000-0003-1064-2149
pubs.begin-page	591
pubs.end-page	613
pubs.issue	3
pubs.organisational-group	School of Medicine
pubs.organisational-group	Duke
pubs.organisational-group	Duke Kunshan University Faculty
pubs.organisational-group	Duke Kunshan University
pubs.organisational-group	Duke Cancer Institute
pubs.organisational-group	Institutes and Centers
pubs.organisational-group	Radiation Oncology
pubs.organisational-group	Clinical Science Departments
pubs.organisational-group	Staff
pubs.organisational-group	Physics
pubs.organisational-group	Trinity College of Arts & Sciences
pubs.publication-status	Published
pubs.volume	77

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