Discovering knowledge from data clustering using automatically-defined interval type-2 fuzzy predicates
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- 作者：Diego S. Comasa ; b ; diego.comas@fi.mdp.edu.ar" class="auth_mail" title="E-mail the corresponding author ; diegoscomas@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Gustavo J. Meschinoc ; gmeschin@fi.mdp.edu.ar" class="auth_mail" title="E-mail the corresponding author ; Ann Nowé ; d ; ann.nowe@vub.ac.be" class="auth_mail" title="E-mail the corresponding author ; Virginia L. Ballarinb ; vballari@fi.mdp.edu.ar" class="auth_mail" title="E-mail the corresponding author
- 关键词：Fuzzy predicates ; Interval type-2 fuzzy logic ; Clustering ; Knowledge-discovering ; Vagueness
- 刊名：Expert Systems with Applications
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：68
- 期：Complete
- 页码：136-150
- 全文大小：1580 K
文摘
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•It is proposed a new clustering method based on interval type-2 fuzzy predicates.
•Fuzzy predicates are automatically generated from data describing clusters.
•Interval type-2 membership functions model variability and vagueness in clusters.
•Linguistic descriptions and knowledge are extracted from predicates.
•The method can be applied to data analysis applications.