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Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.04
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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.

Theme

Data Mining
Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011) 0.03
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Abstract

One of the main themes in text mining is text representation, which is fundamental and indispensable for text-based intellegent information processing. Generally, text representation inludes two tasks: indexing and weighting. This paper has comparatively studied TF*IDF, LSI and multi-word for text representation. We used a Chinese and an English document collection to respectively evaluate the three methods in information retreival and text categorization. Experimental results have demonstrated that in text categorization, LSI has better performance than other methods in both document collections. Also, LSI has produced the best performance in retrieving English documents. This outcome has shown that LSI has both favorable semantic and statistical quality and is different with the claim that LSI can not produce discriminative power for indexing.

Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01

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Date: 22. 8.2014 17:05:18

Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01

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Date: 13. 9.2014 14:45:22

Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01

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Date: 20. 1.2015 18:30:22
18. 9.2018 18:22:56

Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.01

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Date: 11. 6.2012 14:22:34

Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.01

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Date: 22. 3.2013 19:34:49

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