Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Steinley, Douglas; Brusco, Michael J.

doi:10.1007/s11336-007-9019-y

Cited by 120 publications

(87 citation statements)

References 38 publications

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“…For data as displayed in Figure 1, we can expect the clustering procedure to be successful when the dissimilarity measure would incorporate variable selection/weighting. 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).…”

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 Moreover, the available software is out-of-date.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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“…If no target variable is available, then choosing the variables with the lowest degree of intercorrelation or mutual information are reasonable choices. For some data analysis methods there are available methods to guide variable selection, see Steinley and Brusco (2008) for the case of cluster analysis. In our case, due to the availability of domain knowledge, the original set of variables had been previously reduced to a subset of nine variables that was successfully used in the previous experiments with Mentat (Hassan et al 2008).…”

Section: Selection Of Relevant Datamentioning

confidence: 99%

Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling

Arroyo

Hassan

Gutiérrez

et al. 2010

Comput Math Organ Theory

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Agent-based models informed by empirical data are growing in popularity. Many models make extensive use of collected data for the development, initialisation or validation. In parallel, models are growing in size and complexity, generating large amounts of output data. On the other hand, Data Mining is used to extract hidden patterns from large collections of data using different techniques. This work proposes the intense use of Data Mining techniques for the improvement and development of agent-based models. It presents a methodological approach explaining why and when to use Data Mining, with a formal description of each stage of the corresponding process. This is illustrated with a case study, showing the application of the proposed approach step by step.

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“…Brusco (2004) afirma que a seleção de variáveis elimina por completo o efeito indesejável das variáveis que mascaram a definição das estruturas dos clusters. Outras abordagens representativas para seleção de variáveis de clusterização são apresentadas por Raftery & Dean (2006), Bouveyron et al (2007), Steinley & Brusco (2008b), Dean & Raftery (2010) e Bessaoud et al (2012).…”

Section: Introductionunclassified

Seleção de variáveis para clusterização de bateladas produtivas através de ACP e remapeamento kernel

Cervo

Anzanello

2015

Prod.

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ResumoTécnicas de clusterização visam à formação de grupos de observações homogêneas dentro de um mesmo grupo e significativamente distintas das observações inseridas em outros grupos. Em processos industriais cuja produção é apoiada em bateladas, a definição de famílias (grupos) de bateladas com perfis semelhantes auxilia na definição de estratégias de controle e monitoramento desses processos. Este artigo propõe um método para seleção das variáveis de clusterização mais relevantes para formação de famílias de bateladas. Para tanto, integra funções kernel a um novo índice de importância de variáveis gerado a partir dos parâmetros oriundos da Análise de Componentes Principais (ACP). A qualidade dos agrupamentos formados é avaliada através do Silhouette Index (SI). Quando aplicada em três processos produtivos, a sistemática proposta reteve em média 5,16% das variáveis iniciais e elevou o SI médio em 235,4% frente à utilização de todas as variáveis. Um estudo de simulação também é realizado para avaliar a robustez do método. Palavras-chaveAnálise de clusterização. Seleção de variáveis. Kernel. Processos em batelada.

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Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Abstract: cluster analysis, variable selection,

Cited by 120 publications

References 38 publications

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

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

Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling

Seleção de variáveis para clusterização de bateladas produtivas através de ACP e remapeamento kernel

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