Search (4 results, page 1 of 1)

  • × author_ss:"Zhang, J."
  • × language_ss:"e"
  • × year_i:[2000 TO 2010}
  1. Zhang, J.; An, L.; Tang, T.; Hong, Y.: Visual health subject directory analysis based on users' traversal activities (2009) 0.03
    0.025374835 = product of:
      0.06343709 = sum of:
        0.04170945 = weight(_text_:u in 3112) [ClassicSimilarity], result of:
          0.04170945 = score(doc=3112,freq=4.0), product of:
            0.13587062 = queryWeight, product of:
              3.2744443 = idf(docFreq=4547, maxDocs=44218)
              0.041494254 = queryNorm
            0.30697915 = fieldWeight in 3112, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2744443 = idf(docFreq=4547, maxDocs=44218)
              0.046875 = fieldNorm(doc=3112)
        0.02172764 = product of:
          0.04345528 = sum of:
            0.04345528 = weight(_text_:l in 3112) [ClassicSimilarity], result of:
              0.04345528 = score(doc=3112,freq=2.0), product of:
                0.16492525 = queryWeight, product of:
                  3.9746525 = idf(docFreq=2257, maxDocs=44218)
                  0.041494254 = queryNorm
                0.26348472 = fieldWeight in 3112, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.9746525 = idf(docFreq=2257, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3112)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    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.
  2. Gao, J.; Zhang, J.: Clustered SVD strategies in latent semantic indexing (2005) 0.01
    0.006881708 = product of:
      0.03440854 = sum of:
        0.03440854 = weight(_text_:u in 1166) [ClassicSimilarity], result of:
          0.03440854 = score(doc=1166,freq=2.0), product of:
            0.13587062 = queryWeight, product of:
              3.2744443 = idf(docFreq=4547, maxDocs=44218)
              0.041494254 = queryNorm
            0.25324488 = fieldWeight in 1166, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2744443 = idf(docFreq=4547, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1166)
      0.2 = coord(1/5)
    
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  3. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.01
    0.006330398 = product of:
      0.015825994 = sum of:
        0.012288764 = weight(_text_:u in 1211) [ClassicSimilarity], result of:
          0.012288764 = score(doc=1211,freq=2.0), product of:
            0.13587062 = queryWeight, product of:
              3.2744443 = idf(docFreq=4547, maxDocs=44218)
              0.041494254 = queryNorm
            0.0904446 = fieldWeight in 1211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2744443 = idf(docFreq=4547, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1211)
        0.0035372295 = product of:
          0.007074459 = sum of:
            0.007074459 = weight(_text_:h in 1211) [ClassicSimilarity], result of:
              0.007074459 = score(doc=1211,freq=2.0), product of:
                0.10309036 = queryWeight, product of:
                  2.4844491 = idf(docFreq=10020, maxDocs=44218)
                  0.041494254 = queryNorm
                0.06862386 = fieldWeight in 1211, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.4844491 = idf(docFreq=10020, maxDocs=44218)
                  0.01953125 = fieldNorm(doc=1211)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  4. Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.00
    0.0036212734 = product of:
      0.018106367 = sum of:
        0.018106367 = product of:
          0.036212735 = sum of:
            0.036212735 = weight(_text_:l in 231) [ClassicSimilarity], result of:
              0.036212735 = score(doc=231,freq=2.0), product of:
                0.16492525 = queryWeight, product of:
                  3.9746525 = idf(docFreq=2257, maxDocs=44218)
                  0.041494254 = queryNorm
                0.2195706 = fieldWeight in 231, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.9746525 = idf(docFreq=2257, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=231)
          0.5 = coord(1/2)
      0.2 = coord(1/5)