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Abstract

This is the first textbook on formal concept analysis. It gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. Theory and graphical representation are thus closely coupled together. The mathematical foundations are treated thouroughly and illuminated by means of numerous examples. Since computers are being used ever more widely for knowledge processing, formal methods for conceptual analysis are gaining in importance. This book makes the basic theory for such methods accessible in a compact form

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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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Abstract

Objects, attributes, and concepts are basic notations of conceptual knowledge; they are linked by the following four basic relations: an object has an attribute, an object belongs to a concept, an attribute abstracts from a concept, and a concept is a subconcept of another concept. These structural elements are well mathematized in formal concept analysis. Therefore, conceptual knowledge systems can be mathematically modelled in the frame of formal concept analysis. How such modelling may be performed is indicated by an example of a conceptual knowledge system. The formal definition of the model finally clarifies in which ways representation, inference, acquisition, and communication of conceptual knowledge can be mathematically treated

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Source

Applications of combinatorics and graph theory to the biological and social sciences. Ed.: F. Roberts

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Source

Conceptual structures: theory, tools and applications. 6th International Conference on conceptual Structures, ICCS'98, Montpellier, France, August, 10-12, 1998, Proceedings. Ed.: M.L. Mugnier u. M. Chein

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Source

Knowledge organization. 22(1995) no.2, S.78-81