نبذة مختصرة : International audience ; Clustering aims at finding disjoint groups of similar objects in data and is one major task in Machine Learning. It is also gaining more attention in Formal Concept Analysis community in these last years. This paper proposes an original approach to the clustering of complex data based on Formal Concept Analysis (FCA) and Pattern Structures. Stable concepts are considered as cluster candidates and the SOFIA algorithm is used to discover the set of stable concepts in linear time. Then an algorithm inspired by a rare itemset mining algorithm is designed to build a clustering with good properties, i.e., high internal cohesion within a cluster and high external separation between the clusters. Some interestingness measures allowing us to choose the best clustering are discussed. Finally the present approach is compared to some other well-known algorithms such as KMeans, DBScan, and Optic.
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