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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Aug 2018
Research article |
| 30 Aug 2018
| 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
University of Denver, Department of Chemistry and Biochemistry, Denver, USA
now at: Aerosol Devices, Inc., Fort Collins, Colorado, USA
University of Denver, Department of Chemistry and Biochemistry, Denver, USA
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Cited
22 citations as recorded by crossref.
- Machine learning for improved data analysis of biological aerosol using the WIBS S. Ruske et al. 10.5194/amt-11-6203-2018
- Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
- The characterization and quantification of viable and dead airborne biological particles using flow cytometry and double fluorescent staining L. Liang et al. 10.1016/j.jaerosci.2022.106019
- A Controlled Study on the Characterisation of Bioaerosols Emissions from Compost Z. Nasir et al. 10.3390/atmos9100379
- Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration M. Moss et al. 10.3390/atmos14091416
- Variation characteristics of fluorescent biological aerosol particles in Beijing under springtime clean, haze and dusty condition L. Liang et al. 10.1016/j.uclim.2024.102040
- Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers I. Crawford et al. 10.3390/atmos11101039
- Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry J. Xu et al. 10.1016/j.saa.2022.121841
- Real-time measurements of fluorescent aerosol particles in a living laboratory office under variable human occupancy and ventilation conditions S. Patra et al. 10.1016/j.buildenv.2021.108249
- Spectral Intensity Bioaerosol Sensor (SIBS): an instrument for spectrally resolved fluorescence detection of single particles in real time T. Könemann et al. 10.5194/amt-12-1337-2019
- Model-measurement consistency and limits of bioaerosol abundance over the continental United States M. Zawadowicz et al. 10.5194/acp-19-13859-2019
- Investigating the impact of attenuated fluorescence spectra on protein discrimination J. Xu et al. 10.1364/OE.499362
- On the application of scattering matrix measurements to detection and identification of major types of airborne aerosol particles: Volcanic ash, desert dust and pollen J. Gómez Martín et al. 10.1016/j.jqsrt.2021.107761
- Tropical and Boreal Forest – Atmosphere Interactions: A Review P. Artaxo et al. 10.16993/tellusb.34
- Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
- A Modified Spectroscopic Approach for the Real-Time Detection of Pollen and Fungal Spores at a Semi-Urban Site Using the WIBS-4+, Part I E. Markey et al. 10.3390/s22228747
- Recent progress in online detection methods of bioaerosols T. An et al. 10.1016/j.fmre.2023.05.012
- Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
- Investigation of coastal sea-fog formation using the WIBS (wideband integrated bioaerosol sensor) technique S. Daly et al. 10.5194/acp-19-5737-2019
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments J. Francis et al. 10.1038/s41598-023-38100-1
- Model-based unsupervised clustering for distinguishing Cuvier's and Gervais' beaked whales in acoustic data K. Li et al. 10.1016/j.ecoinf.2020.101094
22 citations as recorded by crossref.
- Machine learning for improved data analysis of biological aerosol using the WIBS S. Ruske et al. 10.5194/amt-11-6203-2018
- Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
- The characterization and quantification of viable and dead airborne biological particles using flow cytometry and double fluorescent staining L. Liang et al. 10.1016/j.jaerosci.2022.106019
- A Controlled Study on the Characterisation of Bioaerosols Emissions from Compost Z. Nasir et al. 10.3390/atmos9100379
- Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration M. Moss et al. 10.3390/atmos14091416
- Variation characteristics of fluorescent biological aerosol particles in Beijing under springtime clean, haze and dusty condition L. Liang et al. 10.1016/j.uclim.2024.102040
- Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers I. Crawford et al. 10.3390/atmos11101039
- Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry J. Xu et al. 10.1016/j.saa.2022.121841
- Real-time measurements of fluorescent aerosol particles in a living laboratory office under variable human occupancy and ventilation conditions S. Patra et al. 10.1016/j.buildenv.2021.108249
- Spectral Intensity Bioaerosol Sensor (SIBS): an instrument for spectrally resolved fluorescence detection of single particles in real time T. Könemann et al. 10.5194/amt-12-1337-2019
- Model-measurement consistency and limits of bioaerosol abundance over the continental United States M. Zawadowicz et al. 10.5194/acp-19-13859-2019
- Investigating the impact of attenuated fluorescence spectra on protein discrimination J. Xu et al. 10.1364/OE.499362
- On the application of scattering matrix measurements to detection and identification of major types of airborne aerosol particles: Volcanic ash, desert dust and pollen J. Gómez Martín et al. 10.1016/j.jqsrt.2021.107761
- Tropical and Boreal Forest – Atmosphere Interactions: A Review P. Artaxo et al. 10.16993/tellusb.34
- Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
- A Modified Spectroscopic Approach for the Real-Time Detection of Pollen and Fungal Spores at a Semi-Urban Site Using the WIBS-4+, Part I E. Markey et al. 10.3390/s22228747
- Recent progress in online detection methods of bioaerosols T. An et al. 10.1016/j.fmre.2023.05.012
- Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
- Investigation of coastal sea-fog formation using the WIBS (wideband integrated bioaerosol sensor) technique S. Daly et al. 10.5194/acp-19-5737-2019
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments J. Francis et al. 10.1038/s41598-023-38100-1
- Model-based unsupervised clustering for distinguishing Cuvier's and Gervais' beaked whales in acoustic data K. Li et al. 10.1016/j.ecoinf.2020.101094
Latest update: 02 Jan 2025
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.
We show the systematic application of hierarchical agglomerative clustering (HAC) to...
