A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

• Published in: EURASIP journal on wireless communications and networking Vol. 2021; no. 1; pp. 1 - 16
• Main Authors: Shi, Congming, Wei, Bingtao, Wei, Shoulin, Wang, Wen, Liu, Hai, Liu, Jialei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 15.02.2021; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Clustering; Communications Engineering; Cosine law; Data analysis; Datasets; Edge; Elbow method; Engineering; Fog; Human-centered Computing in Cloud; Information Systems Applications (incl.Internet); Machine learning; Mathematical analysis; Networks; Signal,Image and Speech Processing; Silhouette coefficient; Silhouette coefficient; Elbow method; Clustering; Cosine law; Machine learning

Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to...