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Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
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- Date
- 5. 4.1996 15:29:15
- Source
- Information systems. 22(1997) nos.5/6, S.333-347
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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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- 5. 4.1996 15:29:15
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- Information systems. 22(1997) nos.5/6, S.349-385
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Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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- 1. 6.2010 9:29:57
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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.
- Date
- 21. 7.2016 19:29:19
-
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
-
Advances in knowledge discovery and data mining (1996)
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- Footnote
- Rez. in: JASIS 49(1998) no.4, S.386-387 (F. Exner)
-
Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001)
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- Date
- 29. 3.2002 17:31:17
-
Maaten, L. van den: Learning a parametric embedding by preserving local structure (2009)
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Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014)
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-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- Date
- 2. 4.2000 18:01:22
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Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
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-
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
-
Wattenberg, M.; Viégas, F.; Johnson, I.: How to use t-SNE effectively (2016)
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Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008)
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Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007)
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- Abstract
- Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.
-
Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007)
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-
Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015)
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Cardie, C.: Empirical methods in information extraction (1997)
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- Date
- 6. 3.1999 13:50:29
-
Bath, P.A.: Data mining in health and medical information (2003)
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- Date
- 23.10.2005 18:29:03
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Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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- Date
- 22.11.1998 18:57:22
