A Fuzzy Approach to Discriminant Analysis based on the Results of an Iterative Fuzzy k-Means Method

Francesco Campobasso, Annarita Fanizzi

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. In previous works we proposed a fuzzy approach to discriminant analysis, structured by linearly regressing the degrees of membership of each unit to every groups on the same variables used in a preliminary clustering. In this work we show that non-linear regression models can be used more profitably than linear ones. The applicative case concerns the entrepreneurial propensity of provinces in Central and Southern Italy, even if our methodological proposal was initially conceived to assign new customers to defined groups for Customer Relationship Management (CRM) purposes.

References

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Paper Citation


in Harvard Style

Campobasso F. and Fanizzi A. (2013). A Fuzzy Approach to Discriminant Analysis based on the Results of an Iterative Fuzzy k-Means Method . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 257-264. DOI: 10.5220/0004553802570264


in Bibtex Style

@conference{fcta13,
author={Francesco Campobasso and Annarita Fanizzi},
title={A Fuzzy Approach to Discriminant Analysis based on the Results of an Iterative Fuzzy k-Means Method},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)},
year={2013},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004553802570264},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)
TI - A Fuzzy Approach to Discriminant Analysis based on the Results of an Iterative Fuzzy k-Means Method
SN - 978-989-8565-77-8
AU - Campobasso F.
AU - Fanizzi A.
PY - 2013
SP - 257
EP - 264
DO - 10.5220/0004553802570264