The article discusses a new method called soft bootstrapping for analyzing data clusters. This method involves making random changes to the weights of observations in the data set. Soft bootstrapping is useful for small sample sizes because it ensures that no object is completely left out of the analysis. The researchers compared this new method with traditional resampling techniques and found it to be effective for cluster analysis.