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Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012)
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- Abstract
- In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
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Bella, A. La; Fronzetti Colladon, A.; Battistoni, E.; Castellan, S.; Francucci, M.: Assessing perceived organizational leadership styles through twitter text mining (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.21-31
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Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
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- Source
- Machine learning. 87(2012) no.1, S.33-55
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Chakrabarti, S.: Mining the Web : discovering knowledge from hypertext data (2003)
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- Isbn
- 1-55860-754-4
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Wang, W.M.; Cheung, C.F.; Lee, W.B.; Kwok, S.K.: Mining knowledge from natural language texts using fuzzy associated concept mapping (2008)
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- Date
- 1. 8.2008 14:46:33
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Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011)
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- Content
- Inhalt: 1. Introduction 2. Association Rules and Sequential Patterns 3. Supervised Learning 4. Unsupervised Learning 5. Partially Supervised Learning 6. Information Retrieval and Web Search 7. Social Network Analysis 8. Web Crawling 9. Structured Data Extraction: Wrapper Generation 10. Information Integration
