Cluster Analysis

Bailey, Kenneth D.

doi:10.2307/270894

Cited by 45 publications

(35 citation statements)

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“…A construção dos grupos foi realizada a partir da técnica estatística de análise de agrupamentos (cluster analysis), que ordena os elementos (eventos) em grupos que possuam características semelhantes (Aldenfelder, 1984;Bailey, 1975).…”

Section: Percurso Metodológicounclassified

Condição de exportação e emprego formal na indústria brasileira, 2003-2008*

Trovão

Dedecca

2014

Rev. econ. contemp.

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A recomposição da base produtiva brasileira, após a década 1990, ocorreu em um contexto de economia aberta e exposta à maior concorrência internacional, muitas vezes estabelecida em condições desfavoráveis, mas caracterizada por maior inserção externa da indústria brasileira. Este ensaio analisa os efeitos desse contexto econômico para a geração de empregos no setor industrial, segundo a condição de inserção das empresas no mercado internacional. Busca elucidar se a inserção externa das empresas está associada a perfis ocupacionais mais favoráveis em termos de qualificação e estabilidade do emprego. Os resultados confirmam que a continuidade da trajetória de crescimento com inserção externa representa um vetor relevante para maior qualificação da força de trabalho.

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Section: Percurso Metodológicounclassified

Condição de exportação e emprego formal na indústria brasileira, 2003-2008*

Trovão

Dedecca

2014

Rev. econ. contemp.

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“…Cluster analysis can be viewed either as a means of summarizing a data set or as a means of constructing a topology [12]. Patterns within a valid cluster are more similar to each other than to a pattern belonging to a different cluster.…”

Section: Introductionmentioning

confidence: 99%

“…According to Hartigan (1967Hartigan ( , p. 1140, "a tree may be regarded as a hierarchical grouping structure, in which the objects are grouped into a set of clusters, these clusters are again grouped into a set of clusters of clusters, and so on." Hartigan regards objects, clusters, and clusters of clusters equally as nodes [12]. Hierarchical clustering algorithms can be agglomerative (bottom-up) or divisive (top-down).…”

Section: Introductionmentioning

confidence: 99%

Data Clustering using almost parameter free Differential Evolution technique

Hanuman¹,

Babu²,

Govardhan³

et al. 2010

IJCA

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The paper presents a comparative analysis of data clustering by Particle swarm optimization (PSO) and differential evolution (DE) techniques. It is clearly reveled from the simulation results that almost parameter free optimization technique such as Differential evolution could provide better performance compared to PSO where in many parameters are to be tuned. To exhibit the numerical optimizing capability of DE we have demonstrated the capability of this by optimizing few benchmark functions.

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“…The best understanding of data set can be obtained when several cluster algorithms are tried [35]. Contributions made by engineers [37], social scientists [38], statisticians [36], biologists [39] and psychologists [40] show that the development of clustering methods is interdisciplinary.…”

Section: Missing Data In Cluster Analysismentioning

confidence: 99%

Missing data treatment method on cluster analysis

Koko

Mohamed²

2015

IJASP

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The missing data in household health survey was challenged for the researcher because of incomplete analysis. The statistical tool cluster analysis methodology implemented in the collected data of Sudan's household health survey in 2006. Current research specifically focuses on the data analysis as the objective is to deal with the missing values in cluster analysis. Two-Step Cluster Analysis is applied in which each participant is classified into one of the identified pattern and the optimal number of classes is determined using SPSS Statistics/IBM. However, the risk of over-fitting of the data must be considered because cluster analysis is a multivariable statistical technique. Any observation with missing data is excluded in the Cluster Analysis because like multi-variable statistical techniques. Therefore, before performing the cluster analysis, missing values will be imputed using multiple imputations (SPSS Statistics/IBM). The clustering results will be displayed in tables. The descriptive statistics and cluster frequencies will be produced for the final cluster model, while the information criterion table will display results for a range of cluster solutions.

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Cluster Analysis

Cited by 45 publications

References 0 publications

Condição de exportação e emprego formal na indústria brasileira, 2003-2008*

Condição de exportação e emprego formal na indústria brasileira, 2003-2008*

Data Clustering using almost parameter free Differential Evolution technique

Missing data treatment method on cluster analysis

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