Search (3 results, page 1 of 1)

  • × author_ss:"Boyack, K.W."
  • × year_i:[2000 TO 2010}
  1. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.02
    0.024042416 = product of:
      0.048084833 = sum of:
        0.048084833 = product of:
          0.096169665 = sum of:
            0.096169665 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
              0.096169665 = score(doc=1352,freq=4.0), product of:
                0.17576122 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050191253 = queryNorm
                0.54716086 = fieldWeight in 1352, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.078125 = fieldNorm(doc=1352)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  2. Klavans, R.; Boyack, K.W.: Toward a consensus map of science (2009) 0.01
    0.010200333 = product of:
      0.020400666 = sum of:
        0.020400666 = product of:
          0.04080133 = sum of:
            0.04080133 = weight(_text_:22 in 2736) [ClassicSimilarity], result of:
              0.04080133 = score(doc=2736,freq=2.0), product of:
                0.17576122 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050191253 = queryNorm
                0.23214069 = fieldWeight in 2736, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2736)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    22. 3.2009 12:49:33
  3. Klavans, R.; Boyack, K.W.: Identifying a better measure of relatedness for mapping science (2006) 0.01
    0.010186661 = product of:
      0.020373322 = sum of:
        0.020373322 = product of:
          0.08149329 = sum of:
            0.08149329 = weight(_text_:authors in 5252) [ClassicSimilarity], result of:
              0.08149329 = score(doc=5252,freq=4.0), product of:
                0.22881259 = queryWeight, product of:
                  4.558814 = idf(docFreq=1258, maxDocs=44218)
                  0.050191253 = queryNorm
                0.35615736 = fieldWeight in 5252, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.558814 = idf(docFreq=1258, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5252)
          0.25 = coord(1/4)
      0.5 = coord(1/2)
    
    Abstract
    Measuring the relatedness between bibliometric units (journals, documents, authors, or words) is a central task in bibliometric analysis. Relatedness measures are used for many different tasks, among them the generating of maps, or visual pictures, showing the relationship between all items from these data. Despite the importance of these tasks, there has been little written an how to quantitatively evaluate the accuracy of relatedness measures or the resulting maps. The authors propose a new framework for assessing the performance of relatedness measures and visualization algorithms that contains four factors: accuracy, coverage, scalability, and robustness. This method was applied to 10 measures of journal-journal relatedness to determine the best measure. The 10 relatedness measures were then used as inputs to a visualization algorithm to create an additional 10 measures of journal-journal relatedness based an the distances between pairs of journals in two-dimensional space. This second step determines robustness (i.e., which measure remains best after dimension reduction). Results show that, for low coverage (under 50%), the Pearson correlation is the most accurate raw relatedness measure. However, the best overall measure, both at high coverage, and after dimension reduction, is the cosine index or a modified cosine index. Results also showed that the visualization algorithm increased local accuracy for most measures. Possible reasons for this counterintuitive finding are discussed.