Searching for relevant information on the World Wide Web is often a laborious and frustrating task for casual and experienced users. To help improve searching on the Web based on a better understanding of user characteristics, we address the following research questions: What kind of information would rough set theory shed on user’s web behavior? What kind of rules can we extract from a decision table that summarizes the behavior of users from a set of attributes with multiple values in such a case? What kind of decision rules can be extracted from a decision table using an information theoretic measure? We compared the results of granularity of decision making systems based on rough sets and information theoretic granulation methods. Although the rules extracted from Rough Set(RS) and Information Theoretic(IT) might be equal, yet the interpretation of the decision is richer in the case of RS than in the case of IT.