Abstract
Here the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation. In this paper, an estimation scheme for clustering instability is developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering instability is minimized. The proposed selection criterion's effectiveness is demonstrated on simulations and real examples.
Original language | English (US) |
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Pages (from-to) | 468-477 |
Number of pages | 10 |
Journal | Computational Statistics and Data Analysis |
Volume | 56 |
Issue number | 3 |
DOIs | |
State | Published - Mar 1 2012 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics
Keywords
- Cluster analysis
- K-means
- Spectral clustering
- Stability