Search (47 results, page 3 of 3)

  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  • × year_i:[2010 TO 2020}
  1. Sun, X.; Lin, H.: Topical community detection from mining user tagging behavior and interest (2013) 0.00 
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    Abstract
    With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.
    Type
    a
  2. Frické, M.: Big data and its epistemology (2015) 0.00 
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    Abstract
    The article considers whether Big Data, in the form of data-driven science, will enable the discovery, or appraisal, of universal scientific theories, instrumentalist tools, or inductive inferences. It points out, initially, that such aspirations are similar to the now-discredited inductivist approach to science. On the positive side, Big Data may permit larger sample sizes, cheaper and more extensive testing of theories, and the continuous assessment of theories. On the negative side, data-driven science encourages passive data collection, as opposed to experimentation and testing, and hornswoggling ("unsound statistical fiddling"). The roles of theory and data in inductive algorithms, statistical modeling, and scientific discoveries are analyzed, and it is argued that theory is needed at every turn. Data-driven science is a chimera.
    Type
    a
  3. Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010) 0.00 
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  4. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.00 
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  5. Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014) 0.00 
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  6. Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.00 
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  7. Carter, D.; Sholler, D.: Data science on the ground : hype, criticism, and everyday work (2016) 0.00 
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