A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm
A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm
Abstract 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 discriminate the optimal cluster number, …