I am building a classification model (binary outcome) and would like to include an external cluster membership code as a predictor. For training the model, this is straight forward. When "scoring" new data with the model,  it is less so. 

 - If the clustering was k-means and used euclidean distance, assigning
   the new data to its closest cluster (centroid) would be easy.
 - **But, how do you do this when you are using a distance matrix as input to the clustering algorithm?**

Note: Once the classification model is built, the labeling and meaning of the clusters must remain static.

The question is general, but perhaps made more difficult in that I am looking at clustering time series data (using the `dtw` package with `hclust`) - so it seems that one possible solution to the general question ( i.e. to use a predictive model to map new data to the existing cluster assignments) is not possible.

Any experience with this?