Search (7 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Benoit, G.: Data mining (2002) 0.01 
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    Abstract
    Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.
  2. Zhou, L.; Chaovalit, P.: Ontology-supported polarity mining (2008) 0.01 
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  3. Srinivasan, P.: Text mining : generating hypotheses from MEDLINE (2004) 0.01 
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  4. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.01 
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    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.
  5. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.01 
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  6. Li, J.; Zhang, P.; Cao, J.: External concept support for group support systems through Web mining (2009) 0.01 
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  7. Chen, C.-C.; Chen, A.-P.: Using data mining technology to provide a recommendation service in the digital library (2007) 0.01 
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