Decision analysis of fuzzy partition tree applying AIC and fuzzy decision

Kimiaki Shinkai, Shuya Kanagawa, Takenobu Takizawa, Hajime Yamashita

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    We often use fuzzy graph to analyze inexact information such as sociogram structure ([1] and [2]). Concerning the hierarchical cluster analysis of a fuzzy graph ([3], [4] and [5] ), the number of clusters may have to be decided in the actual cluster analysis. In other word, we woud like to decide the optimal level with a partition tree. Concerning this problem, while AIC method in statistical analysis has been designed by us ([6] and [10]), we will now propose a fuzzy decision method which is based on the evaluation function paying attention to the size and number of clusters at each level.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages572-579
    Number of pages8
    Volume5179 LNAI
    EditionPART 3
    DOIs
    Publication statusPublished - 2008
    Event12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb
    Duration: 2008 Sep 32008 Sep 5

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 3
    Volume5179 LNAI
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
    CityZagreb
    Period08/9/308/9/5

    Fingerprint

    Fuzzy Graph
    Fuzzy Partition
    Fuzzy Decision
    Decision Analysis
    Decision theory
    Cluster analysis
    Number of Clusters
    Cluster Analysis
    Function evaluation
    Evaluation Function
    Statistical Analysis
    Statistical methods
    Partition

    Keywords

    • AIC (Akaike's information criterion)
    • Fuzzy decision
    • Fuzzy graph
    • Optimal level
    • Partition tree

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Shinkai, K., Kanagawa, S., Takizawa, T., & Yamashita, H. (2008). Decision analysis of fuzzy partition tree applying AIC and fuzzy decision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 5179 LNAI, pp. 572-579). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5179 LNAI, No. PART 3). https://doi.org/10.1007/978-3-540-85567-5-71

    Decision analysis of fuzzy partition tree applying AIC and fuzzy decision. / Shinkai, Kimiaki; Kanagawa, Shuya; Takizawa, Takenobu; Yamashita, Hajime.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5179 LNAI PART 3. ed. 2008. p. 572-579 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5179 LNAI, No. PART 3).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Shinkai, K, Kanagawa, S, Takizawa, T & Yamashita, H 2008, Decision analysis of fuzzy partition tree applying AIC and fuzzy decision. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 5179 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 5179 LNAI, pp. 572-579, 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, Zagreb, 08/9/3. https://doi.org/10.1007/978-3-540-85567-5-71
    Shinkai K, Kanagawa S, Takizawa T, Yamashita H. Decision analysis of fuzzy partition tree applying AIC and fuzzy decision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 5179 LNAI. 2008. p. 572-579. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-540-85567-5-71
    Shinkai, Kimiaki ; Kanagawa, Shuya ; Takizawa, Takenobu ; Yamashita, Hajime. / Decision analysis of fuzzy partition tree applying AIC and fuzzy decision. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5179 LNAI PART 3. ed. 2008. pp. 572-579 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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