Research on Burst Terms Detection Based on Entropy Weight Method

Author:

Feng Guo-he,Kong Yong-xin,Mo Xing-qing

Publisher

Springer International Publishing

Reference14 articles.

1. Brian, L., Rakesh, A., Ramakrishan, S.: Discovering trends in text databases. In: Proceedings of KDD 1997, 227–230 (1997)

2. Havre, S., Hetzler, E., Whitney, P.: ThemeRiver: visualizing thematic changes in large document collections. IEEE Trans. Vis. Comput. Graph. 8(1), 9–20 (2002)

3. Soma, R., David, G., William, M.P.: Methodologies for trend detection in textual data mining (2007). https://dimacs.rutgers.edu/~billp/pubs/ETDMethodologics.pdf. Accessed 6 Dec 2018

4. Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Disc. 7(4), 373–397 (2003)

5. Chen, C.M.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Assoc. Inf. Sci. Technol. 57(3), 359–377 (2006)

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