Search (1 results, page 1 of 1)

0.061869845 = product of:
  0.12373969 = sum of:
    0.12373969 = sum of:
      0.07432922 = weight(_text_:web in 2673) [ClassicSimilarity], result of:
        0.07432922 = score(doc=2673,freq=6.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.43716836 = fieldWeight in 2673, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0546875 = fieldNorm(doc=2673)
      0.049410466 = weight(_text_:22 in 2673) [ClassicSimilarity], result of:
        0.049410466 = score(doc=2673,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.2708308 = fieldWeight in 2673, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=2673)
  0.5 = coord(1/2)

Abstract

Examines techniques that discover features in sets of pre-categorized documents, such that similar documents can be found on the WWW. Examines techniques which will classifiy training examples with high accuracy, then explains why this is not necessarily useful. Describes a method for extracting word clusters from the raw document features. Results show that the clustering technique is successful in discovering word groups in personal Web pages which can be used to find similar information on the WWW

Date

1. 8.1996 22:08:06

Footnote

Contribution to a special issue of papers from the 6th International World Wide Web conference, held 7-11 Apr 1997, Santa Clara, California