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I guess you mean cluster_count from the old C implementation of k-means, it's nothing else than the number of clusters to build via k-means (i.e. it's the same like the 'k'-parameter in the c++ or python interface) as GilLevi also pointed out. Btw. you can also use the BoWTrainer-interface: BowKMeansTrainer (http://docs.opencv.org/modules/features2d/doc/object_categorization.html?highlight=bowtrainer#bowtrainer-cluster), which effectively also runs k-means.
For bag of words you give all features from all training images as input (if you have too many features such that they don't fit all in your memory than take a random subset equally distributed for all images, for example you want 150 000 features from 150 images, you select 1000 descriptors randomly from each image).
