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

Association rule mining is a popular technique used to find associations between attributes in a dataset. When using deterministic algorithms, if the attributes have numerical values the usual approach is to discretize them defining proper intervals. But the discretization can notably affect the quality of the rules generated. This work presents a method based on a deterministic exploration of the interval search space without a previous discretization of the numerical attributes. It has been applied to medical data from an atherosclerosis study. The quality of the obtained rules seems to support this method as a valid alternative for this kind of rule extraction.

RULE EXTRACTION FROM MEDICAL DATA WITHOUT DISCRETIZATION OF NUMERICAL ATTRIBUTES