Abstract
Xie-Beni-type cluster validity indices have been often used for evaluating the quality of Fuzzy c-Means (FCM) cluster partitions because they can validate fuzzy partitions considering the geometrical features of clusters, which suit human feelings in most cases. In Xie-Beni-type indices, cluster compactness and separateness are measured by using intra-cluster deviations and inter-cluster distance (distances among cluster centers). In order to apply Xie-Beni-type indices to co-clustering tasks, the compactness and separateness measures must be modified for handling centroid-less partitions. In this paper, the applicability of a Xie-Beni-type co-cluster validity index to Fuzzy Co-clustering of Documents and Keywords (Fuzzy CoDoK) is investigated.
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Muranishi, M., Honda, K., Notsu, A. (2014). Xie-Beni-Type Fuzzy Cluster Validation in Fuzzy Co-clustering of Documents and Keywords. 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_4
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DOI: https://doi.org/10.1007/978-3-319-05515-2_4
Publisher Name: Springer, Cham
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