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:
- Issue Date:
- Research Org.:
- Sandia National Lab
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1079466
- Patent Number(s):
- 8280887
- Application Number:
- 13/042,053
- Assignee:
- Sandia Corporation (Albuquerque, NM)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
H - ELECTRICITY H01 - BASIC ELECTRIC ELEMENTS H01J - ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
- 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}
}
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Works referenced in this record:
Efficient algorithms for agglomerative hierarchical clustering methods
journal, December 1984
- Day, William H. E.; Edelsbrunner, Herbert
- Journal of Classification, Vol. 1, Issue 1
A fast encoding algorithm for vector quantization
journal, December 1997
- Baek, SeongJoon; Jeon, Bumki; Sung, Koeng-Mo
- IEEE Signal Processing Letters, Vol. 4, Issue 12, p. 325-327