The k-means strategy divided the chemicals into four categories consisting of high toxicity, a set of user defined centers (so called centroides) are used for clustering the data. This algorithm has the advantage of incorporating more than one parameter, compared to expert manual judgment in the clustering. Additionally, this algorithm, given that it has randomly selected centroides in the first iteration, it requires further validation. Here we employed bootstrapping to assure the selected acute fish toxicity categories (i.e. clusters) are robust enough for predictive purposes. To do that, the fish toxicity data was randomly divided into 90% training set and 10% test set. The training set then was bootstrapped with replacement for 500 iterations, to guarantee that each model is built based on a unique dataset. The most commonly identified centroid over 500 iterations was selected as final model and for acute fish toxicity categorization. In the end, the final model was further tested using the test set.