Proportional membership in fuzzy clustering as a model of ideal types

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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'.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence
Subtitle of host publicationKnowledge Extraction, Multi-Agent Systems, Logic Programming, and Constraint Solving - 10th Portuguese Conference on Artificial Intelligence, EPIA 2001, Proc.
EditorsPavel Brazdil, Alipio Jorge
PublisherSpringer Verlag
Pages52-62
Number of pages11
Volume2258
ISBN (Print)354043030X, 9783540430308
DOIs
Publication statusPublished - 2001
Event10th Portuguese Conference on Artificial Intelligence, EPIA 2001 - Porto, Portugal
Duration: 17 Dec 200120 Dec 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2258 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Portuguese Conference on Artificial Intelligence, EPIA 2001
Country/TerritoryPortugal
CityPorto
Period17/12/0120/12/01

Keywords

  • Fuzzy clustering
  • Fuzzy model identification
  • Ideal type
  • Proportional membership

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