A Framework for Feature Selection in Clustering

By Daniela M. Witten, Robert Tibshirani. Journal of the American Statistical Association, 2010

720 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3d123b2dbccb6027d4f54f7c671f40bcbf873d2e

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Citations in the corpus

Regularized k-means clustering of high-dimensional data and its asymptotic consistency (2012, methodology): “Focusing on the k-means and hierarchical clustering, [25] proposed a general sparse clustering framework using a similar idea as nonnegative garrote [4], however the asymptotic consistency was not discussed in their framework.”
Robust continuous clustering (2017, background): “Signiﬁcant advances in the last two decades include spectral clustering (2–4), generalizations of classic center-based methods (5, 6), mixture models (7, 8), mean shift (9), afﬁnity propagation (10), subspace clustering (11–13), nonparametric methods (14, 15), and feature selection (16–20).”
Feature Selection for Clustering: A Review (2018, methodology): “This framework can be applied to any similarity-based clustering technique, yet, they used k-means clustering in [71].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “1998; Chan and Hall 2010; Raftery and Dean 2006), model-based methods such as (Pan and Shen 2007; Wang ans Zhu 2008), and criterion-based methods such as (Witten and Tibshirani 2010; Raftery and Dean 2006).”
A review of feature selection methods with applications (2015, neutral): “Feature selection for sparse clustering multivariate, similarity clustering [12]”
A Sparse Singular Value Decomposition Method for High-Dimensional Data (2014, background): “Going one step further, Witten and Tibshirani (2010) used sparsity to develop a novel form of hierarchical clustering.”
Feature selection in machine learning: A new perspective (2018, methodology): “[91] integrated sparse K-means and hierarchical clustering into a feature clustering framework.”
dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering (2015, methodology): “The R language (R Core Team, 2014)—a leading, crossplatform and open source statistical programming environment— has many implementations of HCA algorithms (Chipman and Tibshirani, 2006; Hornik, 2014; Schmidtlein etal., 2010; Witten and Tibshirani, 2010).”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “Witten and Tibshirani [133], and Luss and Aspremont [134] proposed an embedded-based feature selection of”
Web-scale k-means clustering (2010, methodology): “Applying L1 constraints to kmeans clustering has been studied in forthcoming work by Witten andTibshirani[5].”
Model-based clustering of high-dimensional data: A review (2014, methodology): “Several clustering methods can be reformulated from such a criterion and Witten & Tibshirani apply in particular their criterion to the k-means and hierarchical clustering methods.”
A Survey on Multi-View Clustering (2017, neutral): “Although feature selection [151], [152] or feature dimension reduction like PCA is commonly used to alleviate this problem in single-view settings, there are not convincing methods up to now, especially deep learning cannot cope with it due to the properties: small size and high feature dimension.”
Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, background): “Witten and Tibshirani [145] proposed a framework for feature selection in sparse clustering.”
The complex relationship between emotions, approaches to learning, study success and study progress during the transition to university (2016, background): “There are also various elegant regularisation-based approaches (Witten and Tibshirani 2010).”