Abstract
We discuss in this paper a method for finding Top-N Pseudo Formal Concepts. A pseudo formal concept (pseudo FC in short) can be viewed as a natural approximation of formal concepts. It covers several formal concepts as its majorities and can work as a representative of them. In a word, such a pseudo FC is defined as a triple (X, Y, S), where X is a closed set of objects, Y a set of primary features, S a set of secondary features. Then, the concept tells us that 1) all of the objects in X are associated with the primary features Y and 2) for each secondary feature y ∈ S, a majority of X is also associated with y. Therefore, X can be characterized not only exactly by Y but also naturally and flexibly by Y ∪ { y } for each secondary feature y. Our task is formalized as a problem of finding Top-N δ-Valid ( τ, ρ)-Pseudo Formal Concepts. The targets can be extracted based on clique search. We show several pruning and elimination rules are available in our search. A depth-first branch-and-bound algorithm with the rules is designed. Our experimental result shows that a pseudo FC with a natural conceptual meaning can be efficiently extracted.
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Okubo, Y., Haraguchi, M. (2009). Finding Top-N Pseudo Formal Concepts with Core Intents. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_36
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DOI: https://doi.org/10.1007/978-3-642-03070-3_36
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