Clustering is a data mining method, which consists in discovering interesting data distributions in very large databases. The applications of clustering cover customer segmentation, catalog design, store layout, stock market segmentation, etc. In this paper, we consider the problem of discovering similarity-based clusters in a large database of event sequences. We introduce a hierarchical algorithm that uses sequential patterns found in the database to efficiently generate both the clustering model and data clusters. The algorithm iteratively merges smaller, similar clusters into bigger ones until the requested number of clusters is reached. In the absence of a well-defined metric space, we propose the similarity measure, which is used in cluster merging. The advantage of the proposed measure is that no additional access to the source database is needed to evaluate the inter-cluster similarities.