-
Kantardzic, M.: Data mining : concepts, models, methods, and algorithms (2003)
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- Classification
- PZY (FH K)
- GHBS
- PZY (FH K)
-
Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011)
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- Classification
- TZG (FH K)
- GHBS
- TZG (FH K)
-
Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003)
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- Source
- c't. 2003, H.19, S.192-197
-
Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013)
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- Abstract
- Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
-
Borgelt, C.; Kruse, R.: Unsicheres Wissen nutzen (2002)
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- Source
- Spektrum der Wissenschaft. 2002, H.11, S.82-84
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
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- Date
- 22. 3.2013 19:43:01
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
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- Date
- 7. 3.2019 16:32:22
-
Tiefschürfen in Datenbanken (2002)
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- Source
- Spektrum der Wissenschaft. 2002, H.11, S.80-91
-
Nohr, H.: Big Data im Lichte der EU-Datenschutz-Grundverordnung (2017)
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-
Winterhalter, C.: Licence to mine : ein Überblick über Rahmenbedingungen von Text and Data Mining und den aktuellen Stand der Diskussion (2016)
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- Source
- 027.7 Zeitschrift für Bibliothekskultur. 4(2016), H.2
-
Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003)
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- Date
- 22. 4.2003 11:45:36
-
Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008)
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- Date
- 3. 5.1997 8:44:22
-
Jäger, L.: Von Big Data zu Big Brother (2018)
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- Date
- 22. 1.2018 11:33:49
-
Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007)
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-
Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016)
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- Abstract
- This study proposes a new method to construct and trace the trajectory of conceptual development of a research field by combining main path analysis, citation analysis, and text-mining techniques. Main path analysis, a method used commonly to trace the most critical path in a citation network, helps describe the developmental trajectory of a research field. This study extends the main path analysis method and applies text-mining techniques in the new method, which reflects the trajectory of conceptual development in an academic research field more accurately than citation frequency, which represents only the articles examined. Articles can be merged based on similarity of concepts, and by merging concepts the history of a research field can be described more precisely. The new method was applied to the "h-index" and "text mining" fields. The precision, recall, and F-measures of the h-index were 0.738, 0.652, and 0.658 and those of text-mining were 0.501, 0.653, and 0.551, respectively. Last, this study not only establishes the conceptual trajectory map of a research field, but also recommends keywords that are more precise than those used currently by researchers. These precise keywords could enable researchers to gather related works more quickly than before.
-
Raghavan, V.V.; Deogun, J.S.; Sever, H.: Knowledge discovery and data mining : introduction (1998)
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Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003)
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Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009)
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Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013)
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