Proportional Membership in Fuzzy Clustering as a Model of Ideal Types

Authors:
Susana Nascimento;Boris Mirkin;Fernando Moura-Pires
Affiliations:
-;-;-
Venue:
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Year:
2001

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Abstract

The goal of this paper is to further investigate the extreme behaviour of the fuzzy clustering proportional membership model (FCPM) in contrast to the central tendency of fuzzy c-means (FCM). A data set from the field of psychiatry has been used for the experimental study, where the cluster prototypes are indeed extreme, expressing the concept of 'ideal type'. While augmenting the original data set with patients bearing less severe syndromes, it is shown that the prototypes found by FCM are changed towards the more moderate characteristics of the data, in contrast with the almost unchanged prototypes found by FCPM, highlighting its suitability to model the concept of 'ideal type'.