Method for discovering relationships in data by dynamic quantum clustering
Abstract
Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the timedependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wavefunctions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among datapoints through observation of varying dynamicaldistances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.
 Inventors:
 Publication Date:
 Research Org.:
 SLAC National Accelerator Lab., Menlo Park, CA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1356210
 Patent Number(s):
 9,646,074
 Application Number:
 14/482,961
 Assignee:
 The Board of Trustees of the Leland Stanford Junior University SLAC
 DOE Contract Number:
 AC0276SF00515
 Resource Type:
 Patent
 Resource Relation:
 Patent File Date: 2014 Sep 10
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
Citation Formats
Weinstein, Marvin, and Horn, David. Method for discovering relationships in data by dynamic quantum clustering. United States: N. p., 2017.
Web.
Weinstein, Marvin, & Horn, David. Method for discovering relationships in data by dynamic quantum clustering. United States.
Weinstein, Marvin, and Horn, David. 2017.
"Method for discovering relationships in data by dynamic quantum clustering". United States.
doi:. https://www.osti.gov/servlets/purl/1356210.
@article{osti_1356210,
title = {Method for discovering relationships in data by dynamic quantum clustering},
author = {Weinstein, Marvin and Horn, David},
abstractNote = {Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the timedependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wavefunctions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among datapoints through observation of varying dynamicaldistances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 5
}

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the timedependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wavefunctions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among datapoints through observation of varying dynamicaldistances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decompositionmore »

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