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Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010) 0.00

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Date: 1. 6.2010 9:24:31

Cecchini, R.L.; Lorenzetti, C.M.; Maguitman, A.G.; Brignole, N.B.: Multiobjective evolutionary algorithms for context-based search (2010) 0.00

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Date: 1. 6.2010 10:42:49

Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.00
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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.

Wei, F.; Li, W.; Liu, S.: iRANK: a rank-learn-combine framework for unsupervised ensemble ranking (2010) 0.00

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Date: 1. 6.2010 10:18:01

Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.00

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Source: Journal of the Association for Information Science and Technology. 66(2015) no.1, S.40-57

Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.00

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Source: Information processing and management. 50(2014) no.1, S.132-155

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