Hierarchical agglomerative clustering (HAC) analysis has been successfully applied to several sets of ambient data (e.g. Crawford et al., 2015; Robinson et al., 2013) and with respect to standardized particles in the laboratory environment (Ruske et al., 2017). Here we show for the first time a systematic application of HAC to a comprehensive set of laboratory data collected using the wideband integrated bioaerosol sensor (WIBS-4A) (Savage et al., 2017). The impact of particle ratio on HAC results was investigated, showing that clustering quality can vary dramatically as a function of ratio. Six strategies for particle pre-processing were also compared, concluding that using raw fluorescence intensity (without normalizing to particle size) and inputting all data in logarithmic bins consistently produced the highest quality results. A total of 23 one-on-one matchups of individual particles types were investigated. Results showed cluster misclassification of < 15 % for 12 of 17 analytical experiments using one biological and one non-biological particle type each. Inputting fluorescence data using a baseline +3σ threshold produced lower misclassification than when inputting either all particles (without fluorescence threshold) or a baseline +9σ threshold. Lastly, six synthetic mixtures of four to seven components were analyzed. These results show that a range of 12–24 % of fungal clusters were consistently misclassified by inclusion of a mixture of non-biological materials, whereas bacteria and diesel soot were each able to be separated with nearly 100 % efficiency. The study gives significant support to the application of clustering analysis to data from commercial UV-LIF instruments being commonly used for bioaerosol research across the globe and provides practical tools that will improve clustering results within scientific studies as a part of diverse research disciplines.