Summarizing Sequential Data with Closed Partial Orders
Gemma Casas-Garriga · 2005
In this paper we address the task of summarizing a set of input sequences by means of local ordering relationships on items occurring in the sequences. Our goal is not mining these structures directly from the data, but going beyond the idea of closed sequential patterns and generalize it into a novel notion of closed partial order. We will show that just a simple (but not trivial) post-processing of the closed sequences found in the data leads to a compact set of informative closed partial orders. We analyze our proposal not only algorithmically but also theoretically, by showing the connection with Galois lattices. Finally, we illustrate the approach by applying it to real data.