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Clustering Large Data Set: An Applied Comparative Study

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Advanced Statistical Methods for the Analysis of Large Data-Sets

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

The aim of this paper is to analyze different strategies to cluster large data sets derived from social context. For the purpose of clustering, trials on effective and efficient methods for large databases have only been carried out in recent years due to the emergence of the field of data mining. In this paper a sequential approach based on multiobjective genetic algorithm as clustering technique is proposed. The proposed strategy is applied to a real-life data set consisting of approximately 1.5 million workers and the results are compared with those obtained by other methods to find out an unambiguous partitioning of data.

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Authors and Affiliations

  1. Department of Communication and Social Research, Sapienza University of Rome, Via Salaria 113, Rome, Italy

    Laura Bocci & Isabella Mingo

Authors
  1. Laura Bocci
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  2. Isabella Mingo
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Corresponding author

Correspondence to Laura Bocci .

Editor information

Editors and Affiliations

  1. , Dept. of Statistics, University of Roma "La Sapienza", P.le Aldo Moro 5, Roma, 00185, Italy

    Agostino Di Ciaccio

  2. , Dip. di Metodi Quantitativi e Teoria Eco, University "G. d'Annunzio" of Chieti-Pes, V.le Pindaro 42, Pescara, Italy

    Mauro Coli

  3. Fac. Ciencias, Depto. Estadística e Investigación Opera, Universidad de Granada, Avenida Fuente Nueva s/n, Granada, 18071, Spain

    Jose Miguel Angulo Ibanez

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Bocci, L., Mingo, I. (2012). Clustering Large Data Set: An Applied Comparative Study. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_1

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