Search (4 results, page 1 of 1)

0.0028043431 = product of:
  0.0196304 = sum of:
    0.0196304 = product of:
      0.0392608 = sum of:
        0.0392608 = weight(_text_:22 in 1901) [ClassicSimilarity], result of:
          0.0392608 = score(doc=1901,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.30952093 = fieldWeight in 1901, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1901)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Source

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

8.0203614E-4 = product of:
  0.0056142528 = sum of:
    0.0056142528 = product of:
      0.028071264 = sum of:
        0.028071264 = weight(_text_:system in 5083) [ClassicSimilarity], result of:
          0.028071264 = score(doc=5083,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.24605882 = fieldWeight in 5083, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5083)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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

7.9397525E-4 = product of:
  0.0055578267 = sum of:
    0.0055578267 = product of:
      0.027789133 = sum of:
        0.027789133 = weight(_text_:system in 3033) [ClassicSimilarity], result of:
          0.027789133 = score(doc=3033,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.2435858 = fieldWeight in 3033, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3033)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

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

6.8055023E-4 = product of:
  0.0047638514 = sum of:
    0.0047638514 = product of:
      0.023819257 = sum of:
        0.023819257 = weight(_text_:system in 5082) [ClassicSimilarity], result of:
          0.023819257 = score(doc=5082,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 5082, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=5082)
      0.2 = coord(1/5)
  0.14285715 = coord(1/7)

Abstract

The aim of this paper is to indicate how TOSCANA may be extended to allow graphical representations not only of concept lattices but also of concept graphs in the sense of Contextual Logic. The contextual- logic extension of TOSCANA requires the logical scaling of conceptual and relational scales for which we propose the Peircean Algebraic Logic as reconstructed by R. W. Burch. As graphical representations we recommend, besides labelled line diagrams of concept lattices and Sowa's diagrams of conceptual graphs, particular information maps for utilizing background knowledge as much as possible. Our considerations are illustrated by a small information system about the domestic flights in Austria