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Title: 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 time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (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 data-points through observation of varying dynamical-distances 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:
;
Issue Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1162103
Patent Number(s):
8874412
Application Number:
12/586,036
Assignee:
The Board of Trustees of the Leland Stanford Junior University (Palo Alto, CA)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
DOE Contract Number:  
AC02-76SF00515
Resource Type:
Patent
Resource Relation:
Patent File Date: 2009 Sep 15
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., 2014. Web.
Weinstein, Marvin, & Horn, David. Method for discovering relationships in data by dynamic quantum clustering. United States.
Weinstein, Marvin, and Horn, David. Tue . "Method for discovering relationships in data by dynamic quantum clustering". United States. https://www.osti.gov/servlets/purl/1162103.
@article{osti_1162103,
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 time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (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 data-points through observation of varying dynamical-distances 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 = {2014},
month = {10}
}

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Works referenced in this record:

Quantum Clustering Algorithm based on Exponent Measuring Distance
conference, December 2008


Algorithm for Data Clustering in Pattern Recognition Problems Based on Quantum Mechanics
journal, December 2001


Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization
journal, September 2006


Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
journal, July 2006


Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods
journal, May 2005