A framework for feature selection in clustering
Open accessAbstract
We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated data and on genomic data sets.
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Numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis.
“Currently, numerous research efforts are focused on utilizing genetic data and artificial intelligence algorithms to develop diagnostic models to enhance the clinical efficiency and accuracy of breast cancer diagnosis [109,110,111].”
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References 37 · Cited by 696
Research works printed but not linked: 1
- McLachlan GJ Peel D 2000 Finite Mixture Models John Wiley & Sons New York, NY
- Authors
- Witten, D. M., & Tibshirani, R.
- Affiliations
- National Institutes of Health (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2010-06
- Volume
- 105
- Issue
- 490
- Pages
- 713-726
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- PMC
- PMC2930825
- OpenAlex
- W2048178552
- Cited by
- 696
- References
- 37
- Citations by year
- 2026: 34 · 2025: 44 · 2024: 43 · 2023: 48 · 2022: 56 · 2021: 64 · 2020: 56 · 2019: 81 · 2018: 41 · 2017: 56 · 2016: 29 · 2015: 40 · 2014: 35 · 2013: 30 · 2012: 23
- FWCI
- 18.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Statistical Association (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health (n01hv28183-13-0-0)
- National Heart, Lung, and Blood Institute (N01 HV028183)
- National Institute of Biomedical Imaging and Bioengineering (R01EB001988)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- feature selection, sparse clustering, lasso penalty, high-dimensional data, genomic data, adaptive feature selection
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