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

The curse of dimensionality in the realm of association rules is twofold.Firstly, we have the well known exponential increase in computationalcomplexity with increasing item set size. Secondly, there is a \emph{relatedcurse} concerned with the distribution of (spare) data itself in highdimension. The former problem is often coped with by projection, i.e., featureselection, whereas the best known strategy for the latter is avoidance. Thiswork summarizes the first attempt to provide a computationally feasible methodfor measuring the extent of dimension curse present in a data set with respectto a particular class machine of learning procedures. This recent developmentenables the application of various other methods from geometric analysis to beinvestigated and applied in machine learning procedures in the presence of highdimension.

Intrinsic dimension and its application to association rules