Abstract
Handling very large data sets is a significant issue in many applications of data analysis. In Fuzzy c-Means (FCM), several sampling approaches for handling very large data have been proved to be useful. In this paper, the sampling approaches are applied to fuzzy co-clustering tasks for handling cooccurrence matrices composed of many objects. The goal of co-clustering is simultaneously partition both objects and items into co-clusters and item memberships are used for characterizing each co-cluster instead of cluster centers in the conventional FCM. In some modified approaches, item memberships are utilized in conjunction with other objects for inheriting the property of other sample sets.
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Honda, K., Notsu, A., Oh, CH. (2014). Handling Very Large Cooccurrence Matrices in Fuzzy Co-clustering by Sampling Approaches. In: Cho, Y., Matson, E. (eds) Soft Computing in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-319-05515-2_3
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DOI: https://doi.org/10.1007/978-3-319-05515-2_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05514-5
Online ISBN: 978-3-319-05515-2
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