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

There are various cluster validity indices used for evaluating clusteringresults. One of the main objectives of using these indices is to seek theoptimal unknown number of clusters. Some indices work well for clusters withdifferent densities, sizes, and shapes. Yet, one shared weakness of thosevalidity indices is that they often provide only one optimal number ofclusters. That number is unknown in real-world problems, and there might bemore than one possible option. We develop a new cluster validity index based ona correlation between an actual distance between a pair of data points and acentroid distance of clusters that the two points occupy. Our proposed indexconstantly yields several local peaks and overcomes the previously statedweakness. Several experiments in different scenarios, including UCI real-worlddata sets, have been conducted to compare the proposed validity index withseveral well-known ones. An R package related to this new index called NCvalidis available at https://github.com/nwiroonsri/NCvalid.

Clustering performance analysis using a new correlation-based cluster validity index