Journal article
Bootstrapping estimates of stability for clusters, observations and model selection
Computational statistics, Vol.34(1), pp.349-372
03/2019
DOI: 10.1007/s00180-018-0830-y
Abstract
Clustering is a challenging problem in unsupervised learning. In lieu of a gold standard, stability has become a valuable surrogate to performance and robustness. In this work, we propose a non-parametric bootstrapping approach to estimating the stability of a clustering method, which also captures stability of the individual clusters and observations. This flexible framework enables different types of comparisons between clusterings and can be used in connection with two possible bootstrap approaches for stability. The first approach, scheme 1, can be used to assess confidence (stability) around clustering from the original dataset based on bootstrap replications. A second approach, scheme 2, searches over the bootstrap clusterings for an optimally stable partitioning of the data. The two schemes accommodate different model assumptions that can be motivated by an investigator’s trust (or lack thereof) in the original data and additional computational considerations. We propose a hierarchical visualization extrapolated from the stability profiles that give insights into the separation of groups, and projected visualizations for the inspection of the stability of individual operations. Our approaches show good performance in simulation and on real data. These approaches can be implemented using the R package bootcluster that is available on the Comprehensive R Archive Network (CRAN).
Details
- Title: Subtitle
- Bootstrapping estimates of stability for clusters, observations and model selection
- Creators
- Han YuBrian ChapmanArianna Di FlorioEllen EischenDavid GotzMathews JacobRachael Hageman Blair
- Resource Type
- Journal article
- Publication Details
- Computational statistics, Vol.34(1), pp.349-372
- DOI
- 10.1007/s00180-018-0830-y
- ISSN
- 0943-4062
- eISSN
- 1613-9658
- Grant note
- DOI: 10.13039/100000121, name: Division of Mathematical Sciences, award: 1557589, 1557576; DOI: 10.13039/100000121, name: Division of Mathematical Sciences, award: 1557642, 1557668; DOI: 10.13039/100000121, name: Division of Mathematical Sciences, award: 1557593
- Language
- English
- Date published
- 03/2019
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070611502771
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