In many domains (e.g., data mining, data management, data warehouse), a hierarchical organization of attribute values can help the data analysis process. Nevertheless, such hierarchical knowledge does not always available or even may be inadequate or useless when exists. Starting from this consideration, in this paper we tackle the problem of the automatic definition of data-driven taxonomies.To do this we combine techniques coming from information theory and clustering to obtain a structured representation of the at- tribute values: the Contextual Attribute-Value Taxonomy (CAVT). The two main advantages of our method are to be fully unsupervised (i.e., without any knowledge provided by an expert) and parameter-free. We experiments the benefit of use CAVTs in the two following tasks: (i) the multilevel multidimensional sequential pattern mining problem in which hierarchies are involved to exploit abstraction over the data, (ii) the table summarization problem, in which the hierarchies are used to aggregate the data to supply a sketch of the original information to the user. To validate our approach we use real world datasets in which we obtain appreciable results regarding both quantitative and qualitative evaluation.