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A distance and angle similarity measure method

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  • Jin Zhang
  • Robert R. Korfhage

Abstract

This article presents a distance and angle similarity measure. The integrated similarity measure takes the strengths of both the distance and direction of measured documents into account. This article analyzes the features of the similarity measure by comparing it with the traditional distance‐based similarity measure and the cosine measure, providing the iso‐similarity contour, investigating the impacts of the parameters and variables on the new similarity measure. It also gives the further research issues on the topic.

Suggested Citation

  • Jin Zhang & Robert R. Korfhage, 1999. "A distance and angle similarity measure method," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(9), pages 772-778.
    • Handle: RePEc:bla:jamest:v:50:y:1999:i:9:p:772-778
    DOI: 10.1002/(SICI)1097-4571(1999)50:93.0.CO;2-E
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    Cited by:

    1. Takayuki Hayashi, 2003. "Bibliometric analysis on additionality of Japanese R&D programmes," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 301-316, March.
    2. Ting Xiong & Liang Zhou & Ying Zhao & Xiaojuan Zhang, 2022. "Mining semantic information of co-word network to improve link prediction performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 2981-3004, June.
    3. Zhang, Baolong & Wang, Hao & Deng, Sanhong & Su, Xinning, 2020. "Measurement and analysis of Chinese journal discriminative capacity," Journal of Informetrics, Elsevier, vol. 14(1).

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