A simple approach to sparse clustering

Ery Arias-Castro and Xiao Pu

Computational Statistics & Data Analysis, 2017, vol. 105, issue C, 217-228

Abstract: Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others.

Keywords: Sparse clustering; Hill-climbing; High-dimensional; Feature selection (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:105:y:2017:i:c:p:217-228

DOI: 10.1016/j.csda.2016.08.003

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