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

In unsupervised machine learning, agreement between partitions is commonlyassessed with so-called external validity indices. Researchers tend to use andreport indices that quantify agreement between two partitions for all clusterssimultaneously. Commonly used examples are the Rand index and the adjusted Randindex. Since these overall measures give a general notion of what is going on,their values are usually hard to interpret.  Three families of indices based on counting object pairs are analyzed. It isshown that the overall indices can be decomposed into indices that reflect thedegree of agreement on the level of individual clusters. The overall indicesbased on the pair-counting approach are sensitive to cluster size imbalance:they tend to reflect the degree of agreement on the large clusters and providelittle to no information on smaller clusters. Furthermore, the value ofRand-like indices is determined to a large extent by the number of pairs ofobjects that are not joined in either of the partitions.

Understanding partition comparison indices based on counting object pairs