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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...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1017/S1431927617000320

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Department:
Oxford, MPLS, Materials
Role:
Author
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Department:
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:
2017-04-05
Acceptance date:
2017-02-08
DOI:
EISSN:
1435-8115
ISSN:
1431-9276
Pubs id:
pubs:692352
URN:
uri:4f69ff77-0d0d-454e-81e6-52f5d3a35cad
UUID:
uuid:4f69ff77-0d0d-454e-81e6-52f5d3a35cad
Local pid:
pubs:692352

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