Ecological communities consist of a large number of species. Most species are rare or have low abundance, and only a few are abundant and/or frequent. In quantitative community analysis, abundant species are commonly used to interpret patterns of habitat disturbance or ecosystem degradation. Rare species cause many difficulties in quantitative analysis by introducing noises and bulking datasets, which is worsened by the fact that large datasets suffer from difficulties of data handling. In this study we propose a method to reduce the size of large datasets by selecting the most ecologically representative species using a self-organizing map (SOM) and structuring index (SI). As an example, we used diatom community data sampled at 836 sites with 941 species throughout the French hydrosystem. Out of the 941 species, 353 were selected. The selected dataset was effectively classified according to the similarities of community assemblages in the SOM map. Compared to the SOM map generated with the original dataset, the community pattern gave a very similar representation of ecological conditions of the sampling sites, displaying clear gradients of environmental factors between different clusters. Our results showed that this computational technique can be applied to preprocessing data in multivariate analysis. It could be useful for ecosystem assessment and management, helping to reduce both the list of species for identification and the size of datasets to be processed for diagnosing the ecological status of water courses.