Over the last few years, several data mining algorithms have been developed to understand customers' behaviors in e-commerce platforms. They aim to extract knowledge and predict future actions on the website. In this paper we present three algorithms: SEPM-, SEPM+ and SEPM++ (Sequential Event Pattern Mining), for mining sequential frequent patterns. Our goal is to mine clickstream data to extract and analyze useful sequential patterns of clicks. For this purpose, we augment the vertical representation of patterns with additional information about the items' duration. Then based on this representation, we propose the necessary algorithms to mine sequential frequent patterns with the average duration of each of their items. Also, the direction of durations' variation in the sequence is taken into account by the algorithms. This duration is used as a proxy of the interest of the user in the content of the page. Finally, we categorize the resulting patterns and we prove that they are more discriminating than the standard ones. Our approach is tested on real data, and patterns found are analyzed to extract users' discriminatory behaviors. The experimental results on both real and synthetic datasets indicate that our algorithms are efficient and scalable.