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

Classification is widely used technique in the data mining domain, wherescalability and efficiency are the immediate problems in classificationalgorithms for large databases. We suggest improvements to the existing C4.5decision tree algorithm. In this paper attribute oriented induction (AOI) andrelevance analysis are incorporated with concept hierarchys knowledge andHeightBalancePriority algorithm for construction of decision tree along withMulti level mining. The assignment of priorities to attributes is done byevaluating information entropy, at different levels of abstraction for buildingdecision tree using HeightBalancePriority algorithm. Modified DMQL queries areused to understand and explore the shortcomings of the decision trees generatedby C4.5 classifier for education dataset and the results are compared with theproposed approach.

Extracting useful rules through improved decision tree induction using information entropy