Accesso libero

Early identification of scientific breakthroughs through outlier analysis based on research entities

Yang Zhao

Yang Zhao

National Science Library, Chinese Academy of SciencesBeijing, China
Cerca questo autore su
Sciendo|Google Scholar
Zhao, Yang
, 
Mengting Zhang

Mengting Zhang

  • National Science Library, Chinese Academy of SciencesBeijing, China
  • Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of SciencesBeijing, China
Cerca questo autore su
Sciendo|Google Scholar
Zhang, Mengting
, 
Xiaoli Chen

Xiaoli Chen

National Science Library, Chinese Academy of SciencesBeijing, China
Cerca questo autore su
Sciendo|Google Scholar
Chen, Xiaoli
 e 
Zhixiong Zhang

Zhixiong Zhang

  • National Science Library, Chinese Academy of SciencesBeijing, China
  • Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of SciencesBeijing, China
Cerca questo autore su
Sciendo|Google Scholar
Zhang, Zhixiong
  
19 nov 2024
Early identification of scientific breakthroughs through outlier analysis based on research entities's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Scarica la copertina
Categoria dell'articolo: Research Papers
Pubblicato online: 19 nov 2024
Pagine: 90 - 109
Ricevuto: 11 mar 2024
Accettato: 09 ago 2024
DOI: https://doi.org/10.2478/jdis-2024-0027
Parole chiave
© 2024 Yang Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

The overall idea for the identification of scientific breakthroughs through outlier analysis based on research entities.
The overall idea for the identification of scientific breakthroughs through outlier analysis based on research entities.

Figure 2.

Visualization of outliers in award-winning papers based on DBSCAN with different parameter.
Visualization of outliers in award-winning papers based on DBSCAN with different parameter.

Figure 3.

Visualization of outliers in award-winning papers based on Isolation Forest Algorithm with different parameter.
Visualization of outliers in award-winning papers based on Isolation Forest Algorithm with different parameter.

Figure 4.

Evolution of Award-Winning Papers with Optimal Parameter.
Evolution of Award-Winning Papers with Optimal Parameter.

Outlier detection of award-winning papers based on DBSCAN with different parameters_

(ε, m) |O| K cluster center ρc ρavg ρ0
(0.2, 6) 270 13 (1.5761, 0.2596), (-0.2734, 1.0702), (-0.3131, 1.8162), (-1.2773, 1.1308), (-1.5171, -0.9898), (-0.8623, -1.2288), (1.9380, -0.0093), (-0.3877, 0.0042), (2.3369, -0.6248), (-0.2435, -0.9226), (2.2606 -1.4315), (-0.5289, 2.6845), (0.3600, 0.4823) 1.3662 3.6926 1.1378
(0.2, 8) 366 9 (0.1419, 1.4233), (-1.5061, -1.0087), (-0.3125, 1.9862), (-0.2656, 1.0839), (-0.3364, 0.0487), (0.3600, 0.4823), (2.3846, -0.5791), (-0.6100, 1.5679), (-0.5289, 2.6845) 1.6182 3.9896 1.5222
(0.2, 10) 469 5 (-1.5272, -1.1534), (-0.2656, 1.0839), (-1.9954, -0.7550), (-0.3919, 1.9875), (-1.0851, -0.6015) 3.1773 4.4585 2.9030
(0.3, 6) 50 5 (-0.7352, 0.2107), (1.8761, -0.6375), (2.9949, -1.2226), (0.2953, -1.6990), (1.9262, -1.9501) 1.8954 3.4875 0.9506
(0.3, 8) 116 7 (-1.3791, -1.0182), (1.7058, 0.0602), (-0.2563, 1.0866), (-1.2736, 1.0911), (2.0780, -0.9308), (-0.2736 2.8000), (0.5442, 1.9619) 1.4976 3.5744 0.9769
(0.3, 10) 176 6 (1.6912, 0.0958), (-0.3292, 1.5848), (-1.4142, -1.0364), (2.1113, -0.8963), (-0.0728, 0.2179), (-0.3291, 2.7854) 1.4885 3.611 1.0082
(0.4, 6) 21 1 (-0.0612, -0.0326) 3.1290 3.4694 0.5825
(0.4, 8) 34 1 (-0.0883, -0.0065) 3.1468 3.4805 0.8204
(0.4, 10) 52 1 (-0.1230, 0.0339) 3.1678 3.4902 0.8204

Annual and cumulative paper growth in the research field_

Publish year Number of Papers Cumulative Number of Papers Publish year Number of Papers Cumulative Number of Papers Publish year Number of Papers Cumulative Number of Papers
1997 582 582 2006 1,264 9,430 2015 1,680 23,063
1998 650 1,232 2007 1,390 10,820 2016 1,805 24,868
1999 682 1,914 2008 1,491 12,311 2017 1,788 26,656
2000 743 2,657 2009 1,399 13,710 2018 1,788 28,444
2001 908 3,565 2010 1,560 15,270 2019 1,819 30,263
2002 992 4,557 2011 1,519 16,789 2020 1,967 32,230
2003 1,182 5,739 2012 1,489 18,278 2021 2,323 34,553
2004 1,214 6,953 2013 1,527 19,805
2005 1,213 8,166 2014 1,578 21,383
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Informatica, Tecnologia informatica, Project Management, Base dati e data mining
Feed RSS della rivista