I assume HMM will be the most robust to noisy data since it derives a generative model compared to Decision Tree and K-Mean?
Between decision tree and K-mean, which methods is robust to noisy data? I found K-mean may not work well with outliers.
However, with my data (prior knowledge of number of clusters and expected attribute values), K-Mean seems to generate closer outcomes than DT. Can anyone provide any possible reason about this situation?
