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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Clustering techniques play an important role in data mining and its relatedapplications. Among the challenging applications that require robust andreal-time processing are crowd management and group trajectory applications. Inthis paper, a robust and low-complexity clustering algorithm is proposed. It iscapable of processing data in a manner that is shape and centroid independent.The algorithm is of low complexity due to the novel technique to compute thematrix power. The algorithm was tested on real and synthetic data and testresults are reported.

Shape and Centroid Independent Clustring Algorithm for Crowd Management Applications