However, for the task of text categorization, there are usually more than two classes (e. g., 118 categories of news articles for the Reuters-21578 dataset) and with a skewed class distribution (e. g., a lot more news related to "acquisition" than to "grain").
a single support value might lead to some classes generating a tremendous number of features (e. g., hundreds of thousands of frequent subgraphs) and some others only a few (e. g., a few hundreds subgraphs) resulting in a skewed and non-discriminative feature set.