Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm

Soete, Geert De; DeSarbo, Wayne S.; Carroll, J. Douglas

doi:10.1007/bf01908074

Cited by 54 publications

(25 citation statements)

References 30 publications

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“…In partitional clustering the weighting of attributes has received considerable attention (for example, De Sarbo, Carroll, Clarck, and Green, 1984;Steinley and Brusco, 2008;Jain, 2010;Andrews and McNicholas, 2014), but not so for dissimilarity and distance functions. There are studies where attribute weighting is applied, but either these methods are not capable to capture signal in high-dimensional data settings where P >> N, or have as sole purpose to fit a tree in hierarchical clustering (Sebestyen, 1962;De Soete, De Sarbo, and Carroll, 1985;De Soete, 1985;Amorim, 2015). Sparse clustering (SPARCL) by Witten and Tibshirani (2010) can output an attribute weighted dissimilarity measure for the objects.…”

Section: Clustering On Subsets Of Attributesmentioning

confidence: 99%

rCOSA: A Software Package for Clustering Objects on Subsets of Attributes

2017

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rCOSA is a software package interfaced to the R language. It implements statistical techniques for clustering objects on subsets of attributes in multivariate data. The main output of COSA is a dissimilarity matrix that one can subsequently analyze with a variety of proximity analysis methods. Our package extends the original COSA software (Friedman and Meulman, 2004) by adding functions for hierarchical clustering methods, least squares multidimensional scaling, partitional clustering, and data visualization. In the many publications that cite the COSA paper by Friedman and Meulman (2004), the COSA program is actually used only a small number of times. This can be attributed to the fact that this original implementation is not very easy to install and use. Moreover, the available software is out-of-date. Here, we introduce an up-to-date software package and a clear guidance for this advanced technique. The software package and related links are available for free at: https://github.com/mkampert/rCOSA.

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Section: Clustering On Subsets Of Attributesmentioning

confidence: 99%

rCOSA: A Software Package for Clustering Objects on Subsets of Attributes

2017

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“…A reasonably complete literature review of this topic can be found by jointly referencing the articles by DeSarbo et al (1984) and De Soete, DeSarbo, and Carroll (1985). As such, a detailed review will not be repeated here.…”

Section: Introductionmentioning

confidence: 99%

“…In an article published in this journal, De Soete, DeSarbo, and Carroll (1985) presented a variable weighting algorithm to be used with an ultrametric hierarchical clustering procedure. The algorithm determines a weight wk for each of p variables to be used during the computation of the Euclidean distance dissimilarity measure:…”

Section: Introductionmentioning

confidence: 99%

A validation study of a variable weighting algorithm for cluster analysis

Milligan

1989

Journal of Classification

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“…However, techniques developed by Art, Gnanadesikan, and Kettenring (1982) to estimate the pooled within-groups covariance matrix may alleviate this problem (Donoghue, 1994). In addition, clustering procedures recently have been proposed which combine multidimensional scaling and/or variable weighting with specific clustering algorithms (De Soete, DeSarbo, & Carroll, 1985;DeSarbo, Carroll, Clark, & Green, 1984;DeSarbo, Howard, & Jedidi, 1991).…”

Section: The Problem Of Irrelevant Variablesmentioning

confidence: 99%

Variable Screening for Cluster Analysis

Donoghue

1994

ETS Research Report Series

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Inclusion of irrelevant variables in a cluster analysis adversely affects subgroup recovery. This paper examines using moment-based statistics to screen variables; only variables which pass the screening are then used in clustering. Normal mixtures are analytically shown often to possess negative kurtosis. Two related measures, m and coefficient of bimodality b, are also 'examined.A Monte Carlo study compared the screening measures to no selection, De Soete's (1988) ultrametric weights, and Fowlkes, Gnanadesikan, and Kettenring's (1988) forward selection procedure. Screening based on kurtosis degraded recovery and is not recommended.In contrast, screening on m or on b improved recovery over both no selection and forward selection, and screening performed as well as ultrametric weights. Combining screening with ultrametric weights performed extremely well. All methods were found to be somewhat sensitive to other types of error.Screening variables appears a viable alternative to both ultrametric weights and forward selection. The potential advantages and disadvantages of screening are considered.

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Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm

Abstract: Ultrametric trees, Mathematical programming, Variable importance,

Cited by 54 publications

References 30 publications

rCOSA: A Software Package for Clustering Objects on Subsets of Attributes

rCOSA: A Software Package for Clustering Objects on Subsets of Attributes

A validation study of a variable weighting algorithm for cluster analysis

Variable Screening for Cluster Analysis

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