Fuzzy clustering of multi-view relational data with pairwise constraints

Thvs paper presents SS-MVFCVSMdd, a semi-supervised multiview fuzzy clustering algorithm for relational data described by multiple dissimilarity matrices. SS-MVFCVSMdd provides a fuzzy partition in a predetermined number of fuzzy clusters, a representative for each fuzzy cluster, learns a relevance...

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Vydáno v:IEEE International Fuzzy Systems conference proceedings s. 1 - 6
Hlavní autoři: Branco, Diogo P. P., de A T de Carvalho, Francisco
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2017
Témata:
Clustering algorithms
Data mining
Electronic mail
Linear programming
Partitioning algorithms
Resource management
Weight measurement
ISSN:1558-4739
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Shrnutí:Thvs paper presents SS-MVFCVSMdd, a semi-supervised multiview fuzzy clustering algorithm for relational data described by multiple dissimilarity matrices. SS-MVFCVSMdd provides a fuzzy partition in a predetermined number of fuzzy clusters, a representative for each fuzzy cluster, learns a relevance weight for each dissimilarity matrix, and takes into account pairwise constraints must-link and cannot-link, by optimizing a suitable objective function. Experiments with multiview real-valued data sets described by multiple dissimilarity matrices show the usefulness of the proposed algorithm.
ISSN:1558-4739
DOI:10.1109/FUZZ-IEEE.2017.8015529