English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Keywords: KFCM; Type-2 fuzzy sets; Gaussian kernel; MR image. Abstract. While fuzzy c-means is a popular soft clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel width is crucial for effective kernel clustering. Unfortunately, for most applications, it is not easy to find the right width. To design and manage the uncertainty for kernel width, we propose a type-2 kernelized fuzzy c-means algorithm (T2KFCM). We extend the type-1 fuzzy sets of membership to interval type-2 fuzzy sets using two widths and which creates a footprint of uncertainty for the membership. Experiments on MR (Magnetic Resonance) image are given that compare kernelized FCM (KFCM) with T2KFCM. The results show that T2KFCM compares favorably to both of the previous models.

Type-2 kernelized fuzzy c-means algorithm based on the uncertain width of Gaussian kernel with applications in MR image segmentation