A simple approach to sparse clustering

Arias-Castro, Ery; Pu, Xiao

doi:10.1016/j.csda.2016.08.003

Cited by 20 publications

(15 citation statements)

References 25 publications

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“…In addition, the correlations between GEs and CNAs are incorporated into the objective function to accommodate the GE‐regulator regulations, different from the decomposition framework. Recently, for identifying subject groups, the sparse clustering framework has attracted much attention . On one hand, with high‐dimensional omics data, differences across subject clusters are usually only associated with to a small fraction of variables.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, for identifying subject groups, the sparse clustering framework has attracted much attention. 6,7,42 On one hand, with high-dimensional omics data, differences across subject clusters are usually only associated with to a small fraction of variables. The true clustering structures may be missed if too many noninformative variables are used in clustering.…”

Section: Discussionmentioning

confidence: 99%

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Assisted gene expression‐based clustering with AWNCut

Bie

Hidalgo

et al. 2018

Statistics in Medicine

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In the research on complex diseases, gene expression (GE) data have been extensively used for clustering samples. The clusters so generated can serve as the basis for disease subtype identification, risk stratification, and many other purposes. With the small sample sizes of genetic profiling studies and noisy nature of GE data, clustering analysis results are often unsatisfactory. In the most recent studies, a prominent trend is to conduct multidimensional profiling, which collects data on GEs and their regulators (copy number alterations, microRNAs, methylation, etc.) on the same subjects. With the regulation relationships, regulators contain important information on the properties of GEs. We develop a novel assisted clustering method, which effectively uses regulator information to improve clustering analysis using GE data. To account for the fact that not all GEs are informative, we propose a weighted strategy, where the weights are determined data-dependently and can discriminate informative GEs from noises. The proposed method is built on the NCut technique and effectively realized using a simulated annealing algorithm. Simulations demonstrate that it can well outperform multiple direct competitors. In the analysis of TCGA cutaneous melanoma and lung adenocarcinoma data, biologically sensible findings different from the alternatives are made.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assisted gene expression‐based clustering with AWNCut

Bie

Hidalgo

et al. 2018

Statistics in Medicine

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“…It shows that only one objects are clustered incorrectly. This diagram is different from Table 1 where we identified 11 v (with PWCMRD 0.6548) instead of 14 v (with PWC-MRD 0.6584). Table 3(b) describes the detail of the proposed method on Soybean data, all except one object is assigned correctly.…”

Section: Soybeansmall Datamentioning

confidence: 44%

“…self-organizing map network) are used for this purpose. Arias-Castro and Xiao ( [11]) proposed a sparse version of clustering method. Zhang et al ([12]) proposed a novel statistical procedure This paper devotes to find the distinctive attributes among the categorical dataset using pooled relative within-cluster mean difference, then the data is clustered upon a single distinctive attribute.…”

Section: Introductionmentioning

confidence: 99%

Clustering Categorical Data Based on Within-Cluster Relative Mean Difference

Su¹,

Su²

2017

OJS

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The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple method to find such distinctive features by comparing pooled within-cluster mean relative difference and then partition the data upon such features and give subspace of the subgroups. The applications on zoo data and soybean data illustrate the performance of the proposed method.

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“…However, for high-dimensional data, typically only a (small) subset of features are responsible for clustering different groups. Based on this characteristic, many sparse clustering algorithms [4,19,31,39,44,46] have been proposed to simultaneously cluster (high-dimensional) data and select the relevant features. For a detailed overview of these methods, see e.g., Witten and Tibshirani [46] .…”

Section: Introductionmentioning

confidence: 99%

Structured sparse K-means clustering via Laplacian smoothing

Gong

Zhao

Grünewald

2018

Pattern Recognition Letters

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We propose a structured sparse K -means clustering algorithm that learns the cluster assignments and feature weights simultaneously. Compared to previous approaches, including K -means in MacQueen [28] and sparse K -means in Witten and Tibshirani [46], our method exploits the correlation information among features via the Laplacian smoothing technique, so as to achieve superior clustering accuracy. At the same time, the relevant features learned by our method are more structured, hence have better interpretability. The practical benefits of our method are demonstrated through extensive experiments on gene expression data and face images.

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A simple approach to sparse clustering

Cited by 20 publications

References 25 publications

Assisted gene expression‐based clustering with AWNCut

Assisted gene expression‐based clustering with AWNCut

Clustering Categorical Data Based on Within-Cluster Relative Mean Difference

Structured sparse K-means clustering via Laplacian smoothing

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