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A multiple kernels interval type-2 possibilistic C-means

Vu, M.N. and Ngo, L.T. (2016) A multiple kernels interval type-2 possibilistic C-means. In: Asian Conference on Intelligent Information and Database Systems, ACIIDS 2016, 14 March 2016 through 16 March 2016.

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Abstract

In this paper, we propose multiple kernels-based interval type-2 possibilistic c-Means (MKIT2PCM) by using the kernel approach to possibilistic clustering. Kernel-based fuzzy clustering has exhibited quality of clustering results in comparison with “routine” fuzzy clustering algorithms like fuzzy c-Means (FCM) or possibilistic c-Means (PCM) not only noisy data sets but also overlapping between prototypes. Gaussian kernels are suitable for these cases. Interval type-2 fuzzy sets have shown the advantages in handling uncertainty. In this study, multiple kernel method are combined into interval type-2 possibilistic c-Means (IT2PCM) to produce a variant of IT2PCM, called multiple kernels interval type-2 possibilistic c-Means (MKIT2PCM). Experiments on various data-sets with validity indexes show the performance of the proposed algorithms. © Springer International Publishing Switzerland 2016.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1007/978-3-319-31277-4_6
Additional Information: Conference code: 174279. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9878

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