Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques

Savage, Nicole J.; Huffman, J. Alex

doi:https://doi.org/10.5194/amt-11-4929-2018

Articles | Volume 11, issue 8

https://doi.org/10.5194/amt-11-4929-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-4929-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 8

Research article

|

30 Aug 2018

Research article |

| 30 Aug 2018

Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques

Nicole J. Savage and J. Alex Huffman

Download

Final revised paper (published on 30 Aug 2018)
Preprint (discussion started on 07 May 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Review of Savage et al. 2018. Referee comments.', Anonymous Referee #3, 09 May 2018
- AC4: 'Reply to Referee #3', Alex Huffman, 13 Aug 2018
RC2: 'Review of Savage & Huffman 2018', Anonymous Referee #1, 01 Jun 2018
- AC2: 'Reply to Referee #1', Alex Huffman, 13 Aug 2018
RC3: 'Evaluation of a Hierarchical Agglomerative Clustering Method Applied to WIBS Laboratory Data for Improved Discrimination of Biological Particles by Comparing Data Preparation Techniques.', Anonymous Referee #2, 03 Jun 2018
- AC3: 'Reply to Referee #2', Alex Huffman, 13 Aug 2018
SC1: 'One minor correction and some potentially useful comments', S. Ruske, 30 Jun 2018
- AC1: 'Reply to Ruske comment', Alex Huffman, 13 Aug 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Alex Huffman on behalf of the Authors (13 Aug 2018) Author's response Manuscript

ED: Publish as is (19 Aug 2018) by Mingjin Tang

Short summary

We show the systematic application of hierarchical agglomerative clustering (HAC) to comprehensive bioaerosol and non-bioaerosol laboratory data collected with the wideband integrated bioaerosol sensor (WIBS-4A). This study investigated various input conditions and used individual matchups and computational mixtures of particles; it will help improve clustering results applied to data from the ultraviolet laser and light-induced fluorescence instruments commonly used for bioaerosol research.