## AbstractIn the field of Knowledge Discovery, graphs of concepts are an expressive and versatile modeling technique that provides ways to reason about information implicit in the data. Typically, nodes of these graphs represent unstructured closed patterns, such as sets of items, and edges represent the relationships of specificity among them. In this paper we want to consider the case where data keeps an order, and nodes of the concept graph represent complex structured patterns. We contribute by first characterizing a lattice of closed partial orders that precisely summarizes the original ordered data; and second, we show that this lattice can be obtained via coproduct transformations on a simpler graph of so-called stable sequences. In the practice, this graph transformation implies that algorithms for mining plain sequences can efficiently transform the discovered patterns into a lattice of closed partial orders, and so, avoiding the complexity of the mining operation for the partial orders directly from the data.
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