| Title |
Using Principal Component Analysis to Find Correlations and Patterns at Diamond Light Source |
| Authors |
- C. Bloomer, G. Rehm
DLS, Oxfordshire, United Kingdom
|
| Abstract |
Principal component analysis is a powerful data analysis tool, capable of reducing large complex data sets containing many variables. Examination of the principal components set allows the user to spot underlying trends and patterns that might otherwise be masked in a very large volume of data, or hidden in noise. Diamond Light Source archives many gigabytes of machine data every day, far more than any one human could effectively search through for correlations. Presented in this paper are some of the results from running principal component analysis on years of archived data in order to find underlying correlations that may otherwise have gone unnoticed. The advantages and limitations of the technique are discussed.
|
| Paper |
download THPME188.PDF [2.962 MB / 3 pages] |
| Export |
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| Conference |
IPAC2014, Dresden, Germany |
| Series |
International Particle Accelerator Conference (5th) |
| Proceedings |
Link to full IPAC2014 Proccedings |
| Session |
Poster Session, Messi Area |
| Date |
19-Jun-14 16:00–18:00 |
| Main Classification |
06 Instrumentation, Controls, Feedback & Operational Aspects |
| Sub Classification |
T03 Beam Diagnostics and Instrumentation |
| Keywords |
electron, storage-ring, data-analysis, vacuum |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editors |
Christine Petit-Jean-Genaz (CERN, Geneva, Switzerland); Gianluigi Arduini (CERN, Geneva, Switzerland); Peter Michel (HZDR, Dresden, Germany); Volker RW Schaa (GSI, Darmstadt, Germany) |
| ISBN |
978-3-95450-132-8 |
| Published |
July 2014 |
| Copyright |
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