The Lifestyles of Families through Fuzzy C-Means Clustering

  • Silvestro Montrone
  • Paola Perchinunno
  • Samuela L‘Abbate
  • Maria Rosaria Zitolo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8581)


The objective of this report is the analysis of the data arising from the Family Lifestyles survey conducted by the University of Bari “A. Moro” (2012-2013) through the construction of indicators of socio-economic hardship and the identification of family profiles during the current period of crisis. The approach used in this work in order to synthesize and measure the conditions of hardship of a population is based on the so-called “Totally Fuzzy and Relative” method employing a Fuzzy Sets technique in order to obtain a measure of relative incidence in a population from the statistical information provided by a plurality of indicators [1]. The subsequent step involved considering a clustering procedure (Fuzzy c-means) with the objective of outlining various profiles, not defined a priori, to be assigned to each family with different socio-economic behaviours [2]. This clustering method allows, compared to conventional methods, a set of data to belong not only to a main cluster but also to two or more clusters with “fuzzy” profiles.


fuzzy logic fuzzy sets lifestyles 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cheli, B., Lemmi, A.: A Totally Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty. Economic Notes 24(1), 115–134 (1995)Google Scholar
  2. 2.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)CrossRefGoogle Scholar
  3. 3.
    Betti, G., Cheli, B., Lemmi, A.: Occupazione e condizioni di vita su uno pseudo panel italia-no: Primi risultati, avanzamenti e proposte metodologiche. Working paper n. 17. Dip. di Scienze Statistiche, Padova (2002)Google Scholar
  4. 4.
    Lemmi, A., Pannuzi, N.: Continuità e discontinuità nei processi demografici. L’Italia nella transizione demografica, pp. 211–228. 4. Rubettino, Arcavacata di Rende (1995)Google Scholar
  5. 5.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)CrossRefMathSciNetzbMATHGoogle Scholar
  6. 6.
    Dubois, D., Prade, H.: Fuzzy sets and systems. Academic Press, BostonGoogle Scholar
  7. 7.
    Cerioli, A., Zani, S.: A Fuzzy Approach to the Measurement of Poverty. In: Dugum, C., Zenga, M. (eds.) Income and Wealth Distribution, inequality and Poverty. Springer, Berlin (1980)Google Scholar
  8. 8.
    Lemmi, A., Pannuzi, N., Mazzolli, B., Cheli, B., Betti, G.: Misure di povertà multidimensionali e relative: il caso dell’Italia nella prima metà degli anni ‘90. In: Quintano, C. (ed.) Scritti di Statistica Economica, 3, Istituto di Statistica e Matematica, Istituto Universitario Navale di Napoli. Quaderni di Discussione, Curto, Napoli, vol. 13, pp. 263–319 (1997)Google Scholar
  9. 9.
    Dunn: A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters. Journal Cibern 3, 32–57 (1973)CrossRefMathSciNetzbMATHGoogle Scholar
  10. 10.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm. Computer e Geosciences 3, 191–205 (1984)CrossRefGoogle Scholar
  11. 11.
    Bezdek, J.C., Cannon, R.L., Dave, J.V.: Efficient Implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Patters Analysis and Machine Intelligence 8(2), 248–255 (1986)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Silvestro Montrone
    • 1
  • Paola Perchinunno
    • 1
  • Samuela L‘Abbate
    • 1
  • Maria Rosaria Zitolo
    • 1
  1. 1.DISAGUniversity of BariBariItaly

Personalised recommendations