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Conceptual Exploration

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This is the first textbook on attribute exploration (namely, for a given list of attributes, find all meaningful attribute combinations), its theory, its algorithms for applications, and some of its many possible generalizations. Attribute exploration is useful for acquiring structured knowledge through an interactive process, by asking queries to an expert. Generalizations that handle incomplete, faulty, or imprecise data are discussed, but the focus lies on knowledge extraction from a reliable information source. The method is based on Formal Concept Analysis (FCA), a mathematical theory of concepts and concept hierarchies, and uses its expressive diagrams. The presentation is self-contained, in the sense that the reading of the book on FCA by Ganter and Wille (1999) is not mandatory because it provides an introduction to FCA with emphasis on its ability to derive algebraic structures from qualitative data, which can be represented in meaningful and precise graphics.


Machine Learning

Target Group:


Cite as:
B. Ganter, S. Obiedkov (2016), Conceptual Exploration. Springer-Verlag, ISBN 9783662492901.

Author of the review:
Pablo Guerrero-Garcia
University of Malaga


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Andrej KoŇ°ir

Chapter 6 seems applicable to the postprocessing of automatically generated conceptual maps. The approach is not given in terms of the algorithm but still useful in the implementation.