Search (2 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  • × theme_ss:"Data Mining"
  • × year_i:[2000 TO 2010}
  1. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.00 
    0.0037028994 = product of:
  0.022217397 = sum of:
    0.022217397 = weight(_text_:internet in 2563) [ClassicSimilarity], result of:
      0.022217397 = score(doc=2563,freq=2.0), product of:
        0.11352337 = queryWeight, product of:
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.038453303 = queryNorm
        0.1957077 = fieldWeight in 2563, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.046875 = fieldNorm(doc=2563)
  0.16666667 = coord(1/6)
    Abstract
    Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.
  2. Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002) 0.00 
    0.00308575 = product of:
  0.018514499 = sum of:
    0.018514499 = weight(_text_:internet in 5997) [ClassicSimilarity], result of:
      0.018514499 = score(doc=5997,freq=2.0), product of:
        0.11352337 = queryWeight, product of:
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.038453303 = queryNorm
        0.16308975 = fieldWeight in 5997, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
  0.16666667 = coord(1/6)
    Abstract
    Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.

Authors

Types

Subjects