Journal article icon

Journal article

Detecting clusters in atom probe data with Gaussian mixture models.

Abstract:

Accurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probab...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1017/S1431927617000320

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Materials
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
Trinity College
Role:
Author
Publisher:
Oxford University Press Publisher's website
Journal:
Microscopy and Microanalysis Journal website
Volume:
23
Issue:
2
Pages:
269-278
Publication date:
2017-04-01
Acceptance date:
2017-02-08
DOI:
EISSN:
1435-8115
ISSN:
1431-9276
Source identifiers:
692352
Language:
English
Keywords:
Pubs id:
pubs:692352
UUID:
uuid:4f69ff77-0d0d-454e-81e6-52f5d3a35cad
Local pid:
pubs:692352
Deposit date:
2017-05-25

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP