Abstract
Categorization of subjects is a basic approach for summarizing the results of various questionnaires. Co-clustering is a technique for simultaneous co-clustering of mutually familiar objects and items such that each co-cluster is formed by the subject group with their typical questions. This research considers such a situation that a questionnaire is designed for finding some pre-defined categories, which are characterized by some typical questions, but some questions may not be necessarily fit to the target categories. Then, fuzzy co-clustering is performed in conjunction with evaluation of the responsibility of each question for the target categorization. The proposed fuzzy memberships are constructed under a hierarchical structure of category characterization and question evaluation. The characteristic feature of the proposed method is demonstrated through numerical experiments with an artificial data set.
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Acknowledgment
This work was supported in part by JSPS KAKENHI Grant Number JP18K11474.
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Honda, K., Yang, R., Ubukata, S., Notsu, A. (2019). Fuzzy Co-clustering for Categorization of Subjects in Questionnaire Considering Responsibility of Each Question. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_31
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DOI: https://doi.org/10.1007/978-3-030-14815-7_31
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