Fuzzy min–max neural networks: a bibliometric and social network analysis

• Published in: Neural computing & applications Vol. 35; no. 7; pp. 5081 - 5111
• Main Authors: Kenger, Ömer Nedim, Özceylan, Eren
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Bibliometrics; Clustering; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Digital data; Fuzzy logic; Fuzzy sets; Image Processing and Computer Vision; Machine learning; Network analysis; Neural networks; Probability and Statistics in Computer Science; Review; Social network analysis; Social networks; Social network analysis; Hyperbox; Fuzzy min–max neural network; Classification; Clustering; Bibliometric analysis; Data mining
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08267-9

The amount of digital data in the universe is growing at an exponential rate with the rapid development of digital information, and this reveals new machine learning methods. Learning algorithms using hyperboxes are a subsection of machine learning methods. Fuzzy min–max neural network (FMNN) are one of the most common and advanced methods using hyperboxes. FMNN is a special type of NeuroFuzzy system that combines the artificial neural network and fuzzy set into a common framework. This paper conducts an extensive bibliometric and network analysis of FMNN literature. Two hundred and sixty-two publications are analysed from the period of 1992–2022. Several analyses are realized in order to identify trends, challenges and key points in a more scientific and objective way that affect the development of knowledge in the FMNN domain. It can be seen from bibliometric analysis that there is rapid development in the last 10 years. Social network analysis results show that Chee Peng Lim is the most active author in the network. Besides, the modifications of FMNN are generally developed for classification. However, there are still potential future research opportunities for clustering.