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

A vast number of different methods are available for unsupervisedclassification. Since no algorithm and parameter setting performs best in alltypes of data, there is a need for cluster validation to select the actuallybest-performing algorithm. Several indices were proposed for this purposewithout using any additional (external) information. These internal validationindices can be evaluated by applying them to classifications of datasets with aknown cluster structure. Evaluation approaches differ in how they use theinformation on the ground-truth classification. This paper reviews theseapproaches, considering their advantages and disadvantages, and then suggests anew approach.

A new approach for evaluating internal cluster validation indices