A Simple Approach to Sparse Clustering

Ery Arias-Castro; Xiao Pu

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A Simple Approach to Sparse Clustering

Ery Arias-Castro, Xiao Pu

Machine Learning · Submitted 23 Feb 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The new method is shown to be competitive with these two methods and others.

how it is stated · asserted 1.00 read the record →

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,909 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.07 · human participants 0.04 · controlled comparison 0.31 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.97 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; show every role.

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes.	background 1.00	0.06	0.03	asserted 0.90
s1	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.	contribution 0.92	0.81	0.10	asserted 1.00
s2	The new method is shown to be competitive with these two methods and others.	result 1.00	0.89	0.69	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.88.

What was examined, and what was not

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arXiv listing abstract v2

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full text
figures and tables
references and citing sentences
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P abstract

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S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original 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.

Human review

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 7 sentences without a confident label

Uses data or values from 1 paper

Specifically, we consider the same microarray datasets (listed in 10data) used by [ref] to evaluate their IF-PCA method.

Influential Feature PCA for high dimensional clustering · Applications to gene microarray data · 0.92

Compares with 1 paper

We compare our sasc with IF-PCA-HCT [ref] and Sparse K-means [ref] in the setting of iterations.

Influential Feature PCA for high dimensional clustering · A comparison of SAS Clustering with Sparse K-means and IF-PCA-HCT · 1.00

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