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Research on Burst Terms Detection Based on Entropy Weight Method

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Since burst terms are forward-looking and informative, burst term detection (BTD) helps to predict emerging research trends in certain subject areas. However, currently several BTD models are short of simple operation and objective identification. In this paper, we propose a burst term detection model based on entropy weight (entropy-BTD). Under the model, terms were classified into various burst levels by clustering entropy-variation, and the calculation formula of burst comprehensive value was deduced. Experiments showed that three burst classes were identified from clustering: sudden-burst terms, strong-burst terms, and weak-burst terms. The burst terms with high-credibility burst and positive trend were deleted.


  • Burst terms
  • Information entropy
  • Burst detection
  • K-means cluster

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  • DOI: 10.1007/978-3-030-75583-6_14
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  • Brian, L., Rakesh, A., Ramakrishan, S.: Discovering trends in text databases. In: Proceedings of KDD 1997, 227–230 (1997)

    Google Scholar 

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

    CrossRef  Google Scholar 

  • Soma, R., David, G., William, M.P.: Methodologies for trend detection in textual data mining (2007). Accessed 6 Dec 2018

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

    MathSciNet  CrossRef  Google Scholar 

  • 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)

    CrossRef  Google Scholar 

  • Wu, Q.Q., Zhang, H.B., Lan, J.: K-state automaton burst detection model based on KOS: emerging trends in cancer field. J. Inf. Sci. 41(1), 16–26 (2015)

    CrossRef  Google Scholar 

  • Li, Z., Ma, H., Zhou, Y.: A unifying method for outlier and change detection from data streams. In: International Conference on Computational Intelligence and Security (2006)

    Google Scholar 

  • Guo, H.N., Weingart, S., Borner, K.: Mixed-indicators model for identifying emerging research areas. Scientometrics 89(1), 421–435 (2011)

    CrossRef  Google Scholar 

  • Wang, L.Y.: Topic mutation based on keywords mutation. Inf. Stud. Theory Appl. 36(11), 45–48 (2013)

    Google Scholar 

  • Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1949)

    MATH  Google Scholar 

  • Lafouge, T., Michel, C.: Links between information construction and information gain. Entropy and bibliometric distributions. J. Inf. Sci. 27(1), 39–49 (2001)

    Google Scholar 

  • Prathap, G.: The energy-exergy-entropy (or EEE) sequences in bibliometric assessment. Scientometrics 87(3), 515–524 (2011)

    CrossRef  Google Scholar 

  • Li, M., Chu, Y.Q.: Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. J. Inf. Sci. 43(6), 725–741 (2017)

    CrossRef  Google Scholar 

  • Sakai, T., Tamura, K.: Real-time analysis application for identifying bursty local areas related to emergency topics. SpringerPlus 4(1), 1–7 (2015)

    CrossRef  Google Scholar 

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The work was supported by Guangzhou Science and technology plan project “Theory, method and application of Burst Terms Detection” (Grant numbers 202002030384). This is an outcome of the project “Research on Domain Hot Spot Detection Theory, Method and Application” (No. 2019WKXMO043) supported by Graduate Innovation Project of South China Normal University.

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Feng, Gh., Kong, Yx., Mo, Xq. (2021). Research on Burst Terms Detection Based on Entropy Weight Method. In: Bucciarelli, E., Chen, SH., Corchado, J.M., Parra D., J. (eds) Decision Economics: Minds, Machines, and their Society. DECON 2020. Studies in Computational Intelligence, vol 990. Springer, Cham.

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