Search (3 results, page 1 of 1)

  • × theme_ss:"Data Mining"
  • × theme_ss:"Automatisches Klassifizieren"
  1. 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) 0.02 
    0.018966276 = product of:
  0.08850929 = sum of:
    0.021217827 = weight(_text_:subject in 5997) [ClassicSimilarity], result of:
      0.021217827 = score(doc=5997,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.19758089 = fieldWeight in 5997, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
    0.03364573 = weight(_text_:classification in 5997) [ClassicSimilarity], result of:
      0.03364573 = score(doc=5997,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.35186368 = fieldWeight in 5997, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
    0.03364573 = weight(_text_:classification in 5997) [ClassicSimilarity], result of:
      0.03364573 = score(doc=5997,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.35186368 = fieldWeight in 5997, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5997)
  0.21428572 = coord(3/14)
    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.
    Content
    Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.
    Series
    Proceedings of the ... annual conference of the Gesellschaft für Klassifikation e.V. ; 24)(Studies in classification, data analysis, and knowledge organization
  2. Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016) 0.02 
    0.015061316 = product of:
  0.07028614 = sum of:
    0.02018744 = weight(_text_:classification in 3015) [ClassicSimilarity], result of:
      0.02018744 = score(doc=3015,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 3015, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
    0.02018744 = weight(_text_:classification in 3015) [ClassicSimilarity], result of:
      0.02018744 = score(doc=3015,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 3015, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=3015)
    0.029911257 = product of:
      0.059822515 = sum of:
        0.059822515 = weight(_text_:texts in 3015) [ClassicSimilarity], result of:
          0.059822515 = score(doc=3015,freq=2.0), product of:
            0.16460659 = queryWeight, product of:
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.03002521 = queryNorm
            0.36342722 = fieldWeight in 3015, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.4822793 = idf(docFreq=499, maxDocs=44218)
              0.046875 = fieldNorm(doc=3015)
      0.5 = coord(1/2)
  0.21428572 = coord(3/14)
    Abstract
    We analyze the linguistic evolution of selected scientific disciplines over a 30-year time span (1970s to 2000s). Our focus is on four highly specialized disciplines at the boundaries of computer science that emerged during that time: computational linguistics, bioinformatics, digital construction, and microelectronics. Our analysis is driven by the question whether these disciplines develop a distinctive language use-both individually and collectively-over the given time period. The data set is the English Scientific Text Corpus (scitex), which includes texts from the 1970s/1980s and early 2000s. Our theoretical basis is register theory. In terms of methods, we combine corpus-based methods of feature extraction (various aggregated features [part-of-speech based], n-grams, lexico-grammatical patterns) and automatic text classification. The results of our research are directly relevant to the study of linguistic variation and languages for specific purposes (LSP) and have implications for various natural language processing (NLP) tasks, for example, authorship attribution, text mining, or training NLP tools.
  3. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.01 
    0.0067974646 = product of:
  0.04758225 = sum of:
    0.023791125 = weight(_text_:classification in 967) [ClassicSimilarity], result of:
      0.023791125 = score(doc=967,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 967, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=967)
    0.023791125 = weight(_text_:classification in 967) [ClassicSimilarity], result of:
      0.023791125 = score(doc=967,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 967, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=967)
  0.14285715 = coord(2/14)
    Abstract
    Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.

Authors

Years

Types

Subjects