TF*IDF is a common approach used for text mining and information retrieval. We have described a method for using 112 variations on the TF*IDF equation for the classification of 588 CNN news articles belonging to 12 different classes. We found that no single TF*IDF could accurately classify all the documents. In fact, the highest accuracy attainable by any single TF*IDF was 45%. In this article, we take the work further to show how different measurements utilizing the TF*IDF classification results can be used to show that some classes may be logically inconsistent as classes. These methods also may be used to create more cohesive classes.