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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cluster analysis, which focuses on the grouping and categorization of similarelements, is widely used in various fields of research. A novel and fastclustering algorithm, fission clustering algorithm, is proposed in recent year.In this article, we propose a robust fission clustering (RFC) algorithm and aself-adaptive noise identification method. The RFC and the self-adaptive noiseidentification method are combine to propose a self-adaptive robust fissionclustering (SARFC) algorithm. Several frequently-used datasets were applied totest the performance of the proposed clustering approach and to compare theresults with those of other algorithms. The comprehensive comparisons indicatethat the proposed method has advantages over other common methods.

A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle