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

AOI-HEP (Attribute Oriented Induction High Emerging Pattern) as new data mining technique has been success to mine frequent pattern and is extended to mine similar patterns. AOI-HEP is success to mine 3 and 1 similar patterns from IPUMS and breast cancer UCI machine learning datasets respectively. Meanwhile, the experiments showed that there was no finding similar patterns on adult and census UCI machine learning datasets. The experiments showed that finding AOI-HEP similar pattern in dataset is influenced by learning on chosen high level concept attribute in concept hierarchy and it is applied to AOI-HEP frequent pattern in previous research as well. The experiments chosed high level concept attributes such as workclass, clump thickness, means and marts for adult, breast cancer, census and IPUMS datasets respectively. In order to proof that the chosen high level concept attribute will influences the AOI-HEP similar pattern in dataset, then extended experiments were carried on and the finding were census dataset which had been none AOI-HEP similar pattern, had AOI-HEP similar pattern when learned on high level concept in marital attribute. Meanwhile, Breast cancer which had been had 1 AOI-HEP similar pattern, had none AOI-HEP similar pattern when learned on high level concept in attributes such as cell size, cell shape and bare nuclei. The 2 of 3 finding Similar patterns in IPUMS dataset have strong discriminant rule since having large growth rates such as 1.53% and 3.47%, and having large supports in target dataset such as 4.54% and 5.45 respectively. Moreover, there have small supports in contrasting dataset such as 2.96% and 1.57% respectively.

MINING SIMILAR PATTERN WITH ATTRIBUTE ORIENTED INDUCTION HIGH LEVEL EMERGING PATTERN (AOI-HEP) DATA MINING TECHNIQUE