I am attempting to use the bag-of-words approach to examine a large text data set. I am experimenting with using spherical K-means to cluster either documents or terms with respect to the other. I have gotten some promising results. However, I am unable to determine a good strategy for choosing a number of clusters.
When using K-means clustering, I can plot the within-group sum of squares (SSW) and look for the "elbow" of the curve to identify the point of diminishing returns.
Since spherical K-means does not minimize distance, SSW does not seem like the correct metric. Is there some useful measure to determine when additional clusters will provide diminishing returns in spherical K-means?