Conceptual exploration / Bernhard Ganter and Sergei Obiedkov

Por: Ganter, Bernhard [,autor]Tipo de material: TextoTextoEditor: New York, NY : Springer Berlin Heidelberg, 2016Descripción: xvii, 315 páginas : ilustracionesTipo de contenido: text Tipo de medio: unmediated Tipo de portador: volumeISBN: 9783662492901 Tema(s): MINERIA DE DATOS | CIENCIA DE LA COMPUTACION | ALGEBRA | ESTRUCTURAS ALGEBRAICAS ORDENADASClasificación CDD: 512.2
Contenidos:
1. Concept lattices -- 2. An algorithm for closure systems -- 3. The canonical basis -- 4. Attribute exploration -- 5. Exploring data tables -- 6. More expressive variants of exploration.
Resumen: This is the first textbook on attribute exploration, 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, a mathematical theory of concepts and concept hierarchies, and uses its expressive diagrams. The presentation is self-contained. It provides an introduction to Formal Concept Analysis with emphasis on its ability to derive algebraic structures from qualitative data, which can be represented in meaningful and precise graphics.
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Colección General 512.2 G211 (Navegar estantería(Abre debajo)) 1 Disponible 3560902081205

1. Concept lattices -- 2. An algorithm for closure systems -- 3. The canonical basis -- 4. Attribute exploration -- 5. Exploring data tables -- 6. More expressive variants of exploration.

This is the first textbook on attribute exploration, 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, a mathematical theory of concepts and concept hierarchies, and uses its expressive diagrams. The presentation is self-contained. It provides an introduction to Formal Concept Analysis with emphasis on its ability to derive algebraic structures from qualitative data, which can be represented in meaningful and precise graphics.