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Lindig’s algorithm for concept lattices over graded attributes. (English) Zbl 1181.68269

Torra, Vicenç (ed.) et al., Modeling decisions for artificial intelligence. 4th international conference, MDAI 2007, Kitakyushu, Japan, August 16–18, 2007. Proceedings. Berlin: Springer (ISBN 978-3-540-73728-5/pbk). Lecture Notes in Computer Science 4617. Lecture Notes in Artificial Intelligence, 156-167 (2007).
Summary: Formal concept analysis (FCA) is a method of exploratory data analysis. The data is in the form of a table describing relationship between objects (rows) and attributes (columns), where table entries are grades representing degrees to which objects have attributes. The main output of FCA is a hierarchical structure (so-called concept lattice) of conceptual clusters (so-called formal concepts) present in the data. This paper focuses on algorithmic aspects of FCA of data with graded attributes. Namely, we focus on the problem of generating efficiently all clusters present in the data together with their subconcept-superconcept hierarchy. We present theoretical foundations, the algorithm, analysis of its efficiency, and comparison with other algorithms.
For the entire collection see [Zbl 1123.68012].

MSC:

68T30 Knowledge representation
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