One advantage of the two-stage approach is that the same clusterings may be used for different problems or different components of the same system.

One nagging issue with K—Means clustering is how to set k. We show that this question may not need to be answered because we can use clusterings with different k’s at the same time and let the discriminative classifier cherry-pick the clusters at different granularities according to the supervised data.

We can easily use multiple clusterings in feature extraction.

When we extract features from multiple clusterings , the selection of the top-N clusters is done separately for each clustering.

The best result is achieved with multiple phrasal clusterings .