Abstract
Clustering for categorical multivariate data is an important task for summarizing co-occurrence information that consists of mutual affinity among objects and items. This work focus on two fuzzy clustering methods for categorical multivariate data. One of the serious limitations for these methods is the local optimality problem. In this work, an algorithm is proposed to address this issue. The proposed algorithm incorporates multiple token search generated from the eigen decomposition of the Hessian of the objective function. Numerical experiments using an artificial dataset shows that the proposed algorithm is valid.
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Suzuki, K., Kanzawa, Y. (2022). On an Multi-directional Searching Algorithm for Two Fuzzy Clustering Methods for Categorical Multivariate Data. In: Honda, K., Entani, T., Ubukata, S., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2022. Lecture Notes in Computer Science(), vol 13199. Springer, Cham. https://doi.org/10.1007/978-3-030-98018-4_15
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DOI: https://doi.org/10.1007/978-3-030-98018-4_15
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