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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

High dimensional data poses a challenge to traditional clustering algorithms, where the similarity measures are not meaningful, affecting the quality of the groups. As a result, subspace clustering algorithms have been proposed as an alternative, aiming to find all groups in all spaces of the dataset. By detecting groups on lower dimensional spaces, each group may belong to different subspaces of the original dataset. Therefore, attributes the user considers of interest may be excluded in some or all groups, decreasing the value of the result for the data analysts. In this project, a new algorithm is proposed, that combines SUBCLU and the clustering algorithms by constraint, which allows the users to identify variables as attributes of interest based on prior knowledge of domain, targeting direct group detection toward spaces that include user’s attributes of interest, and thereafter, generating more meaningful groups.

tecnología en marcha

Algoritmo semisupervisado de agrupamiento que combina SUBCLU y el agrupamiento basado en restricciones, para la detección de grupos en conjuntos de alta dimensionalidad