# 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}

}

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