Tratamento de dados faltantes empregando biclusterização com imputação múltipla / Treatment of missing data using biclustering with multiple imputation

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

22/06/2011

RESUMO

The answers provided by recommender systems can be interpreted as missing data to be imputed considering the knowledge associated with the available data and the relation between the available and the missing data. There is a wide range of techniques for data imputation, and this work is concerned with multiple imputation. Alternative approaches for multiple imputation have already been proposed, and this work takes biclustering as an effective, flexible and promising strategy. To this end, firstly it is performed a parameter sensitivity analysis of the SwarmBcluster algorithm, recently proposed to implement biclustering and already adapted, in the literature, to accomplish single imputation of missing data. This analysis has indicated that a proper choice of parameters may significantly improve the performance of the algorithm. Secondly, SwarmBcluster was extended to implement multiple imputation, being compared with the well-known NORM algorithm. The quality of the obtained results is computed considering diverse metrics, which reveal that biclustering guides to imputations of better quality in the majority of the experiments

ASSUNTO(S)

dados faltantes ( estatística) sistemas de recomendação cluster algoritmos evolutivos mineração de dados (computação) missing data (statistics) recommender systems cluster evolutionary algorithms data mining

ACESSO AO ARTIGO

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