Discovering frequent and interesting patterns is an important area of data mining. Transactional databases cannot serve the requirement of analyzing current trends in shopping; it is required to focus on analyzing dynamic data sets. Existing data mining algorithms when applied on dynamic data sets takes lot of time as they generate very huge number of frequent patterns making the analyst with the task to go through all the rules and discover interesting ones. Works that are reported until now in reducing number of rules are either time consuming or does not consider the interestingness of the user and does not focus on analysis of rules. This paper extends SSFPOA algorithm which produces clusters of semantically similar frequent patterns and presents these clusters using data visualization.