20.500.12556/RUL-139603 Evolving Gaussian on-line clustering in social network analysis 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. evolving clustering Twitter data analysis online method Gaussian probability samorazvijajoče se rojenje analiza Twitter podatkov sprotna metoda Gaussova verjetnost true false true Angleški jezik Slovenski jezik Članek v reviji 2022-09-05 14:25:24 2022-09-05 14:25:25 2022-09-19 11:51:01 0000-00-00 00:00:00 2022 0 0 8 str. art. 117881 Vol. 207 30 Nov. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 681.5 0957-4174 10.1016/j.eswa.2022.117881 112620547 1-s2.0-S0957417422011320-main.pdf 1-s2.0-S0957417422011320-main.pdf 1 762244C46DA0DD195A490075542DB29E 5a2be706690f85ba4e1951a26e5bb01b825a80b48602038d98f775f160d62d5f 5a01d7af-2d16-11ed-92af-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=160189 https://www.sciencedirect.com/science/article/pii/S0957417422011320 1 c5b9846c-2d15-11ed-92af-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=160188 Fakulteta za elektrotehniko 0 0 0