Résumé
Nowadays, the management of sequential patterns data is an increasing need in many biological data mining and knowledge discovery processes. Indeed, due to the large number of sequential patterns extracted, an efficient interpretation of the results is difficult. Thus, biologists are waiting for new approaches in order to help them during this analysis. Therefore, the development of new data mining techniques for sequential patterns becomes a crucial need. One of the most common data mining and knowledge discovery processes are clustering algorithms. However, there is not too much literature about the application of clustering algorithms to gene sequential patterns due to the difficulty of applying such algorithms to this kind of data. In this paper, we introduce a new gene sequential patterns similarity function and summarization algorithm. We illustrate the feasibility of both contributions combining them into an hierarchical clustering algorithm.