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Evolving Gaussian on-line clustering in social network analysis
ID
Škrjanc, Igor
(
Avtor
),
ID
Andonovski, Goran
(
Avtor
),
ID
Iglesias Martínez, José Antonio
(
Avtor
),
ID
Sesmero, María Paz
(
Avtor
),
ID
Sanchis, Araceli
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(908,63 KB)
MD5: 762244C46DA0DD195A490075542DB29E
URL - Izvorni URL, za dostop obiščite
https://www.sciencedirect.com/science/article/pii/S0957417422011320
Galerija slik
Izvleček
In this paper, we present an evolving data-based approach to automatically cluster Twitter users according to their behavior. The clustering method is based on the Gaussian probability density distribution combined with a Takagi–Sugeno fuzzy consequent part of order zero (eGauss0). This means that this method can be used as a classifier that is actually a mapping from the feature space to the class label space. The eGauss method is very flexible, is computed recursively, and the most important thing is that it starts learning ‘‘from scratch’’. The structure adapts to the new data using adding and merging mechanisms. The most important feature of the evolving method is that it can process data from thousands of Twitter profiles in real time, which can be characterized as a Big Data problem. The final clusters yield classes of Twitter profiles, which are represented as different activity levels of each profile. In this way, we could classify each member as ordinary, very active, influential and unusual user. The proposed method was also tested on the Iris and Breast Cancer Wisconsin datasets and compared with other methods. In both cases, the proposed method achieves high classification rates and shows competitive results.
Jezik:
Angleški jezik
Ključne besede:
evolving clustering
,
Twitter data analysis
,
online method
,
Gaussian probability
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FE - Fakulteta za elektrotehniko
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2022
Št. strani:
8 str.
Številčenje:
Vol. 207, art. 117881
PID:
20.500.12556/RUL-139603
UDK:
681.5
ISSN pri članku:
0957-4174
DOI:
10.1016/j.eswa.2022.117881
COBISS.SI-ID:
112620547
Datum objave v RUL:
05.09.2022
Število ogledov:
497
Število prenosov:
95
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Expert systems with applications
Skrajšan naslov:
Expert syst. appl.
Založnik:
Elsevier
ISSN:
0957-4174
COBISS.SI-ID:
171291
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
samorazvijajoče se rojenje
,
analiza Twitter podatkov
,
sprotna metoda
,
Gaussova verjetnost
Projekti
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Universidad Carlos III de Madrid, Chair of Excellence
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Bank of Santander
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Spain, Ministry of Economy, Industry and Competitiveness
Številka projekta:
RTI2018-096036-B-C22/AEI/10.13039/501100011033
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Spain, Ministry of Economy, Industry and Competitiveness
Številka projekta:
PEAVAUTO-CM-UC3M
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Spain, Ministry of Economy, Industry and Competitiveness
Številka projekta:
PID2019-104793RB-C31
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Spain, Ministry of Economy, Industry and Competitiveness
Številka projekta:
MCIN/AEI/10.13039/501100011033
Financer:
EC - European Commission
Akronim:
NextGenerationEU/PRTR
Financer:
Drugi - Drug financer ali več financerjev
Program financ.:
Region of Madrid, Excellence Program
Številka projekta:
EPUC3M17
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