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

K-means algorithm is a very popular clustering algorithm which is famous forits simplicity. Distance measure plays a very important rule on the performanceof this algorithm. We have different distance measure techniques available. Butchoosing a proper technique for distance calculation is totally dependent onthe type of the data that we are going to cluster. In this paper anexperimental study is done in Matlab to cluster the iris and wine data setswith different distance measures and thereby observing the variation of theperformances shown.

Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab