Search (1 results, page 1 of 1)

  • × author_ss:"Ruiz-Shulcloper, J."
  • × theme_ss:"Data Mining"
  1. Pons-Porrata, A.; Berlanga-Llavori, R.; Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques (2007) 0.02 
    0.023198929 = product of:
  0.046397857 = sum of:
    0.046397857 = product of:
      0.092795715 = sum of:
        0.092795715 = weight(_text_:news in 916) [ClassicSimilarity], result of:
          0.092795715 = score(doc=916,freq=2.0), product of:
            0.26705483 = queryWeight, product of:
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.05094824 = queryNorm
            0.34747815 = fieldWeight in 916, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.2416887 = idf(docFreq=635, maxDocs=44218)
              0.046875 = fieldNorm(doc=916)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.