An increasing number of data sources is published on the Web, expressed using the languages proposed by the W3C such as RDF. In these sources, data is not constrained by a schema: data could differ from the schema-related statements provided in the source; furthermore, the schema could be incomplete of even missing; this makes the use of the data sources difficult. Some works have addressed the problem of automatic schema discovery but their scalability and their use in a big data context remains a challenge. In this work, we address this scalability issue, which is mainly related to the clustering algorithms at the core of schema discovery. In order to process large amounts of data, we propose to built a condensed representation of the initial dataset by extracting patterns representing all the existing combinations of properties. The clustering is then performed on the patterns instead of the initial dataset. In this paper, we describe our approach, and present its implementation using a big data technology. We also present some experimental evaluations performed on real datasets.