Estimating the number of clusters in multivariate data by various fittings of the \(L\)-curve
From MaRDI portal
Publication:6653951
DOI10.1007/s40314-024-02839-8MaRDI QIDQ6653951
Publication date: 17 December 2024
Published in: Computational and Applied Mathematics (Search for Journal in Brave)
hierarchical clustering\(k\)-meansgap statisticssilhouette methodelbow methodR-indexangle indexCalinski and Harabaz indexCSum indexforward-backward indexHartigan indexKrzanowski and Lai index
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Graphical methods in statistics (62A09)
Cites Work
- Unnamed Item
- Unnamed Item
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- On the number of clusters
- A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering
- Finding Groups in Data
- Well-Separated Clusters and Optimal Fuzzy Partitions
- Printer graphics for clustering
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Clustering for Sparsely Sampled Functional Data
- Determining the Number of Clusters Using the Weighted Gap Statistic
- An automated robust algorithm for clustering multivariate data
This page was built for publication: Estimating the number of clusters in multivariate data by various fittings of the \(L\)-curve