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A Proposal for a Discriminant Analysis Based on the Results of a Preliminary Fuzzy Clustering

  • Francesco Campobasso
  • Annarita Fanizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7974)

Abstract

The common classification techniques are designed for a rigid (even if probabilistic) allocation of each unit into one of several groups. Nevertheless the dissimilarity among combined units often leads to consider the opportunity of assigning each of them to more than a single group with different degrees of membership. The same logic can be applied in attributing a new observation to previously identified fuzzy groups. This paper precisely presents a proposal for a discriminant analysis, structured by regressing the degrees of membership to every groups of each unit on the same variables used in a preliminary clustering. Such a proposal, initially conceived to assign new customers to defined groups for Customer Relationship Management (CRM) purposes, is now tested in an applicative case concerning the entrepreneurial propensity of the sampled provinces of Central and Southern Italy, in which an iterative fuzzy k-means method is preliminary used to split them into an optimal number of homogeneous groups.

Keywords

Fuzzy discriminant analysis fuzzy clustering linear regression model customer relationship management entrepreneurial propensity 

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References

  1. 1.
    Campobasso, F., Montrone, S., Perchinunno, P., Fanizzi, A.: A Fuzzy Approach to the Small Area Estimation of Poverty in Italy. In: Phillips-wren, G., Nakamatsu, K., Jain, L.C., Howlett, R.J. (eds.) Advances in Intelligent Decision Technologies. SIST, vol. 4, pp. 309–318. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Campobasso, F., Fanizzi, A., Perchinunno, P.: Homogenous urban poverty clusters within the city of Bari. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 232–244. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    UnionCamera, Italain atlas of the competitiveness of provinces and regions, http://www.unioncamere.gov.it/Atlante/
  4. 4.
    National Institute for Foreign Trade, Italy in the International Economy - Summary Report for 2008-2009, http://www.slideshare.net/Maryss82/situazione-di-internazionalizzazione-italia-2009-ice
  5. 5.
    Campobasso, F., Fanizzi, A.: A fuzzy approach to Ward’s method of classification: an application case to the Italian university system. In: Montrone, S., Perchinunno, P. (eds.) Statistical Methods for Spatial Planning and Monitoring, pp. 31–46. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Kaufman, L., Rousseau, P.J.: Finding Groups in Data - An Introduction to Cluster Analysis. John Wiley and Sons, New York (1990)CrossRefGoogle Scholar
  7. 7.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Campobasso
    • 1
  • Annarita Fanizzi
    • 1
  1. 1.Department of Economics and MathematicsUniversity of BariBariItaly

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