Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors.