Search (38 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Formale Begriffsanalyse"
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  1. Ganter, B.; Stahl, J.; Wille, R.: Conceptual measurement and many-valued contexts (1986) 0.03 
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    Language
    e
  2. Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995) 0.02 
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    Language
    e
    Source
    Knowledge organization. 22(1995) no.2, S.78-81
  3. Priss, U.: Faceted information representation (2000) 0.02 
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    Date
    22. 1.2016 17:47:06
    Language
    e
  4. Priss, U.: Faceted knowledge representation (1999) 0.02 
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    Date
    22. 1.2016 17:30:31
    Language
    e
  5. Kumar, C.A.; Radvansky, M.; Annapurna, J.: Analysis of Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for information retrieval (2012) 0.01 
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  6. Hereth, J.; Stumme, G.; Wille, R.; Wille, U.: Conceptual knowledge discovery and data analysis (2000) 0.01 
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    Abstract
    In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin
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  7. Wille, R.: Knowledge acquisition by methods of formal concept analysis (1989) 0.00 
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    Language
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    Source
    Data analysis, learning symbolic and numeric knowledge. Hrsg.: E. Diday
  8. Wille, R.: Line diagrams of hierarchical concept systems (1984) 0.00 
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  9. Wille, R.: Geometric representations of concept lattices (1989) 0.00 
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  10. Wille, R.: Lattices in data analysis : how to draw them with a computer (1989) 0.00 
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  11. Wille, R.: Concept lattices and conceptual knowledge systems (1992) 0.00 
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  12. Priss, U.: Formal concept analysis in information science (2006) 0.00 
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  13. Ganter, B.; Wille, R.: Conceptual scaling (1989) 0.00 
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  14. Vogt, F.; Wachter, C.; Wille, R.: Data analysis based on a conceptual file (1991) 0.00 
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  15. Vogt, F.; Wille, R.: TOSCANA - a graphical tool for analyzing and exploring data (1995) 0.00 
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  16. Scheich, P.; Skorsky, M.; Vogt, F.; Wachter, C.; Wille, R.: Conceptual data systems (1993) 0.00 
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  17. Rusch, A.; Wille, R.: Knowledge spaces and formal concept analysis (1996) 0.00 
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  18. Reinartz, T.P.; Zickwolff, M.: ¬Two conceptual approaches to acquire human expert knowledge in a complex real world domain (1996) 0.00 
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  19. Groh, B.; Strahringer, S.; Wille, R.: TOSCANA-systems based on thesauri (1998) 0.00 
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  20. De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012) 0.00 
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    Abstract
    In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.
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