Search (16 results, page 1 of 1)

Survey of text mining : clustering, classification, and retrieval (2004) 0.02

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BK: 54.72 / Künstliche Intelligenz
Classification: ST 302 Informatik / Monographien / Künstliche Intelligenz / Expertensysteme; Wissensbasierte Systeme
54.72 / Künstliche Intelligenz
RVK: ST 302 Informatik / Monographien / Künstliche Intelligenz / Expertensysteme; Wissensbasierte Systeme

Information visualization in data mining and knowledge discovery (2002) 0.01

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BK: 54.72 / Künstliche Intelligenz
Classification: 54.72 / Künstliche Intelligenz
Date: 23. 3.2008 19:10:22

Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.01

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Date: 2. 4.2000 18:01:22

KDD : techniques and applications (1998) 0.01

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Footnote: A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01

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Date: 22.11.1998 18:57:22

Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01

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Source: Information systems. 22(1997) nos.5/6, S.333-347

Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.00

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Source: Information systems. 22(1997) nos.5/6, S.349-385

Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00

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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) 0.00

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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) 0.00

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Date: 7. 3.2019 16:32:22

Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008) 0.00

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Source: Kompatibilität, Medien und Ethik in der Wissensorganisation - Compatibility, Media and Ethics in Knowledge Organization: Proceedings der 10. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation Wien, 3.-5. Juli 2006 - Proceedings of the 10th Conference of the German Section of the International Society of Knowledge Organization Vienna, 3-5 July 2006. Ed.: H.P. Ohly, S. Netscher u. K. Mitgutsch

Dang, X.H.; Ong. K.-L.: Knowledge discovery in data streams (2009) 0.00

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Song, J.; Huang, Y.; Qi, X.; Li, Y.; Li, F.; Fu, K.; Huang, T.: Discovering hierarchical topic evolution in time-stamped documents (2016) 0.00

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Kantardzic, M.: Data mining : concepts, models, methods, and algorithms (2003) 0.00

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Classification: PZY (FH K)
GHBS: PZY (FH K)

Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011) 0.00

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Classification: TZG (FH K)
GHBS: TZG (FH K)

Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.00
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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.

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