A Comparative Study on Questionnaire Design for Categorization Based on Fuzzy Co-clustering Concept and Multi-view Possibilistic Partition

This paper considers the problem of designing a questionnaire for categorization of subjects. In order to well-organize the categorical structures of subjects, co-cluster information is utilized based on two different co-clustering approaches under multi-view situations. A comparative experiment stu...

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Bibliographic Details
Published inInternational Conference on Fuzzy Theory and Its Applications (Print) pp. 1 - 4
Main Authors Yang, Ruixin, Honda, Katsuhiro, Ubukata, Seiki, Notsu, Akira
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.11.2019
Subjects
Batch production systems
categorization
Classification algorithms
Clustering algorithms
co-clustering
Data mining
fuzzy clustering
Linear programming
Optimization
Task analysis
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ISSN2377-5831
DOI10.1109/iFUZZY46984.2019.9066247

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Summary:This paper considers the problem of designing a questionnaire for categorization of subjects. In order to well-organize the categorical structures of subjects, co-cluster information is utilized based on two different co-clustering approaches under multi-view situations. A comparative experiment studies the characteristics of fuzzy co-clustering-based categorization, where all categories are simultaneously extracted in a batch process, and possibilistic partition-based categorization, where each co-cluster is independently extracted under each view of categories.
ISSN:2377-5831
DOI:10.1109/iFUZZY46984.2019.9066247