Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 105, Issue 490, Pages 713-726Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/jasa.2010.tm09415
Keywords
Hierarchical clustering; High-dimensional; K-means clustering; Lasso; Model selection; Sparsity; Unsupervised learning
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Funding
- National Defense Science and Engineering Graduate Fellowship
- National Science Foundation [DMS-9971405]
- National Institutes of Health [N01-HV-28183]
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL028183] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB001988] Funding Source: NIH RePORTER
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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 and genomic data.
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