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

Different cluster techniques based on the Rough Set Theory (RST) have been used for attribute selection and grouping objects displaying similar characteristics. On the other hand, a majority of these clustering techniques cannot tackle uncertainty. Furthermore, these processes are computationally complicated and less accurate. In this study, the researchers have explored the limitations of the two rough set theory based techniques, i.e., the Maximum Dependency Attribute (MDA) and the Maximum Indiscernible Attribute (MIA). They also proposed a novel approach for selecting the clustering attributes, i.e., the Maximum Mean Attribute (MMA). They compared the performances of the MMA, MDA and the MIA techniques, using the UCI dataset. Their results validated the performance of the MMA with regards to its accuracy and computational complexity. Keywords— clustering, rough set theory, categorical data, dependency of attribute, performance.

Rough Set -Algorithm for Clustering Categorical Data Using Mean Attribute (MMA) Dependency Based Measure