Kernel-based Fuzzy Co-clustering in Feature Space with Automated Variable Weighting

Kernel functions have been used successfully in clustering algorithms to deal with the separability of clusters efficiently. Bringing this idea to co-clustering, we propose two kernel-based fuzzy co-clustering algorithms based on the fuzzy double Kmeans (FDK). The first proposed algorithm, the Gauss...

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Vydáno v:IEEE International Fuzzy Systems conference proceedings s. 1 - 8
Hlavní autoři: de Sa, Jose Nataniel A., Ferreira, Marcelo R.P., de Carvalho, Francisco de A.T.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 18.07.2022
Témata:
Clustering algorithms
Entropy
Indexes
Kernel
Measurement
Partitioning algorithms
Prototypes
ISSN:1558-4739
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Shrnutí:Kernel functions have been used successfully in clustering algorithms to deal with the separability of clusters efficiently. Bringing this idea to co-clustering, we propose two kernel-based fuzzy co-clustering algorithms based on the fuzzy double Kmeans (FDK). The first proposed algorithm, the Gaussian kernel fuzzy double Kmeans (GKFDK), is based on FDK and computes the cluster prototypes in the original feature space. The second algorithm, the Weighted gaussian kernel fuzzy double Kmeans (WGKFDK), is an extension of the GKFDK with automated variable weighting, that distinguishes the relevance of the variables in each cluster. Experiments performed with both synthetic and real data, in comparison with previous state-of-the-art co-clustering algorithms, showed the effectiveness of the proposed algorithms.
ISSN:1558-4739
DOI:10.1109/FUZZ-IEEE55066.2022.9882838