Search (1 results, page 1 of 1)

  • × author_ss:"Tang, T."
  • × author_ss:"Zhang, J."
  • × language_ss:"e"
  1. Zhang, J.; An, L.; Tang, T.; Hong, Y.: Visual health subject directory analysis based on users' traversal activities (2009) 0.00
    0.0035313342 = product of:
      0.014125337 = sum of:
        0.014125337 = product of:
          0.056501348 = sum of:
            0.056501348 = weight(_text_:based in 3112) [ClassicSimilarity], result of:
              0.056501348 = score(doc=3112,freq=8.0), product of:
                0.14144066 = queryWeight, product of:
                  3.0129938 = idf(docFreq=5906, maxDocs=44218)
                  0.04694356 = queryNorm
                0.39947033 = fieldWeight in 3112, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  3.0129938 = idf(docFreq=5906, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3112)
          0.25 = coord(1/4)
      0.25 = coord(1/4)
    
    Abstract
    Concerns about health issues cover a wide spectrum. Consumer health information, which has become more available on the Internet, plays an extremely important role in addressing these concerns. A subject directory as an information organization and browsing mechanism is widely used in consumer health-related Websites. In this study we employed the information visualization technique Self-Organizing Map (SOM) in combination with a new U-matrix algorithm to analyze health subject clusters through a Web transaction log. An experimental study was conducted to test the proposed methods. The findings show that the clusters identified from the same cells based on path-length-1 outperformed both the clusters from the adjacent cells based on path-length-1 and the clusters from the same cells based on path-length-2 in the visual SOM display. The U-matrix method successfully distinguished the irrelevant subjects situated in the adjacent cells with different colors in the SOM display. The findings of this study lead to a better understanding of the health-related subject relationship from the users' traversal perspective.