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Title: Hierarchical clustering using correlation metric and spatial continuity constraint

Abstract

Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

Inventors:
;
Publication Date:
Research Org.:
Sandia National Lab
Sponsoring Org.:
USDOE
OSTI Identifier:
1079466
Patent Number(s):
8,280,887
Application Number:
13/042,053
Assignee:
Sandia Corporation (Albuquerque, NM) SNL-A
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Stork, Christopher L., and Brewer, Luke N. Hierarchical clustering using correlation metric and spatial continuity constraint. United States: N. p., 2012. Web.
Stork, Christopher L., & Brewer, Luke N. Hierarchical clustering using correlation metric and spatial continuity constraint. United States.
Stork, Christopher L., and Brewer, Luke N. Tue . "Hierarchical clustering using correlation metric and spatial continuity constraint". United States. https://www.osti.gov/servlets/purl/1079466.
@article{osti_1079466,
title = {Hierarchical clustering using correlation metric and spatial continuity constraint},
author = {Stork, Christopher L. and Brewer, Luke N.},
abstractNote = {Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {10}
}

Patent:

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