-
KDD : techniques and applications (1998)
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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
- Source
- Knowledge-based systems. 10(1998) no.7, S.401-470
-
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
-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- Date
- 2. 4.2000 18:01:22
-
Knowledge discovery and data mining (1998)
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- Date
- 7. 2.1999 11:18:28
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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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)
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- Source
- Information systems. 22(1997) nos.5/6, S.333-347
-
Principles of data mining and knowledge discovery (1998)
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- Isbn
- 3-540-65068-7
-
Chardonnens, A.; Hengchen, S.: Text mining for cultural heritage institutions : a 5-step method for cultural heritage institutions (2017)
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- Date
- 7. 4.2017 19:18:05
-
Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.349-385
-
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.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410
-
Wong, S.K.M.; Butz, C.J.; Xiang, X.: Automated database schema design using mined data dependencies (1998)
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- Date
- 7. 2.1999 11:16:36
-
Raghavan, V.V.; Deogun, J.S.; Sever, H.: Knowledge discovery and data mining : introduction (1998)
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- Date
- 7. 2.1999 11:23:06
-
Relational data mining (2001)
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- Isbn
- 3-540-42289-7
-
Fenstermacher, K.D.; Ginsburg, M.: Client-side monitoring for Web mining (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.7, S.625-637
-
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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- Date
- 7. 2.2013 19:25:40
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Sun, X.; Lin, H.: Topical community detection from mining user tagging behavior and interest (2013)
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- Date
- 7. 2.2013 19:31:28
-
Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1668-1678
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Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018)
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- Journal of the Association for Information Science and Technology. 69(2018) no.7, S.879-889
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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
- Isbn
- 978-3-642-19459-7
-
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
