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

A method of tendency mining in dynamic association rule based on compatibility feature vector SVM classifier is proposed. Firstly, the class association rule set named CARs is mined by using the method of tendency mining in dynamic association rules. Secondly, the algorithm of SVM is used to construct the classifier based on compatibility feature vector to classify the gotten CARs taking its advantage when dealing with high complex data. It uses a method based on judging rules' weight to construct the model. And at last the method is compared with the traditional methods by their mining accuracy. The method can solve the problem of high time complexity and have a higher accuracy than the traditional methods which is helpful to make mining dynamic association rules more accurate and effective. By analyzing the final results it's proved that the method has lower complexity and higher classification accuracy.

Tendency Mining in Dynamic Association Rules Based on SVM Classifier