Variable selection in clustering

Fowlkes, Edward B.; Gnanadesikan, R.; Kettenring, J. R.

doi:10.1007/bf01897164

Cited by 131 publications

(85 citation statements)

References 8 publications

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“…Fowlkes et al (1988) used a forward selection approach in the context of complete linkage hierarchical clustering. The variables are added using information of the between-cluster and total sum of squares, and their significance is judged based on graphical information.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

“…As pointed out by several authors (e.g., Fowlkes, Gnanadesikan, and Kettering 1988;Milligan 1989;Gnanadesikan, Kettering, and Tao 1995;Brusco and Cradit 2001), the inclusion of unnecessary covariates could complicate or even mask the recovery of the clusters. Common approaches to mitigating the effect of noisy variables or identifying those that define true cluster structure involve differentially weighting the covariates or selecting the discriminating ones.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Variable Selection in Clustering High-Dimensional Data

Tadesse

Sha

Vannucci

2005

Journal of the American Statistical Association

215

219

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Over the last decade, technological advances have generated an explosion of data with substantially smaller sample size relative to the number of covariates ( p n). A common goal in the analysis of such data involves uncovering the group structure of the observations and identifying the discriminating variables. In this article we propose a methodology for addressing these problems simultaneously. Given a set of variables, we formulate the clustering problem in terms of a multivariate normal mixture model with an unknown number of components and use the reversible-jump Markov chain Monte Carlo technique to define a sampler that moves between different dimensional spaces. We handle the problem of selecting a few predictors among the prohibitively vast number of variable subsets by introducing a binary exclusion/inclusion latent vector, which gets updated via stochastic search techniques. We specify conjugate priors and exploit the conjugacy by integrating out some of the parameters. We describe strategies for posterior inference and explore the performance of the methodology with simulated and real datasets.

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Section: Review Of Existing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection in Clustering High-Dimensional Data

Tadesse

Sha

Vannucci

2005

Journal of the American Statistical Association

215

219

View full text Add to dashboard Cite

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“…They used the adjusted Rands (1971) index by Hubert and Arabie (1985) to measure the agreement of partitions. Brusco and Cradit (2001) developed a heuristic variable-selection procedure, VS-KM(variableselection heuristic for K-means clustering) that builds on the strengths of HINoV and adds variables in a forward manner as well as uses information about the between-cluster and total-sum-of-squares, similar to the Fowlkes et al (1988) method. This procedure begins by selecting first two variables considering the adjusted Rand index and the ratio of between cluster sum-of squares to the total sumof-squares, and then adds a variable in a forward manner.…”

Section: Review Of Variable Selection and Outlier Detection For K-meamentioning

confidence: 99%

Variable Selection and Outlier Detection for Automated K-means Clustering

Kim

2015

CSAM

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An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/∼sskim/SVOKmeans.r.

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“…Dimension reduction techniques (like principal component analysis) will produce linear combinations of the variables which are difficult to interpret unless most of the coefficients of the linear combination are negligent. The variable selection method of Fowlkes, et al (1988) shifts the problem to a reduced variable space and looks for new clusters with less variables. Tandesse, et al (2005) propose a Bayesian approach for simultaneously selecting variables and identifying cluster structures without knowing the number of clusters.…”

Section: Introductionmentioning

confidence: 99%

Selection of Variables for Cluster Analysis and Classification Rules

Fraiman

Justel

Svarc

2008

Journal of the American Statistical Association

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In this paper we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly oriented to detect the "noisy" non-informative variables, while the other deals also with multicolinearity. A forward-backward algorithm is also proposed to make feasible these procedures in large data sets. A small simulation is performed and some real data examples are analyzed.

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Variable selection in clustering

Abstract: Variable selection, Cluster analysis of two-mode data, scaling of variables, Pillai trace statistic, Interactive data analysis,

Cited by 131 publications

References 8 publications

Bayesian Variable Selection in Clustering High-Dimensional Data

Bayesian Variable Selection in Clustering High-Dimensional Data

Variable Selection and Outlier Detection for Automated K-means Clustering

Selection of Variables for Cluster Analysis and Classification Rules

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