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

Density-based clustering could be the most popular clustering algorithm sinceit can identify clusters of arbitrary shape as long as different (high-density)clusters are separated by low-density regions. However, the requirement of theseparateness of clusters by low-density regions is not trivial since ahigh-density region might have different structures which should be clusteredinto different groups. Such a situation demonstrates the main flaw of allprevious density-based clustering algorithms we have known--structures in ahigh-density cluster could not be detected. Therefore, this paper aims toprovide a density-based clustering scheme that not only has the abilityprevious ones have but could also detect structures in a high-density regionnot separated by low-density ones. The algorithm employs secondary directeddifferential, hierarchy, normalized density, as well as the self-adaptioncoefficient, and thus is called Structure Detecting Cluster by HierarchicalSecondary Directed Differential with Normalized Density and Self-Adaption,dubbed by SDC-HSDD-NDSA for short. To illustrate its effectiveness, we run thealgorithm in several data sets. The results verify its validity in structuredetection, robustness over noises, as well as independence of granularities,and demonstrate that it could outperform previous ones. The Python code of thepaper could be found on https://github.com/Hao-B-Shu/SDC-HSDD-NDSA.

SDC-HSDD-NDSA: Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption