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/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

In this paper we make two novel contributions to hierarchical clustering.First, we introduce an anomalous pattern initialisation method for hierarchicalclustering algorithms, called A-Ward, capable of substantially reducing thetime they take to converge. This method generates an initial partition with asufficiently large number of clusters. This allows the cluster merging processto start from this partition rather than from a trivial partition composedsolely of singletons. Our second contribution is an extension of the Ward andWard p algorithms to the situation where the feature weight exponent can differfrom the exponent of the Minkowski distance. This new method, called A-Wardp\b{eta} , is able to generate a much wider variety of clustering solutions. Wealso demonstrate that its parameters can be estimated reasonably well by usinga cluster validity index. We perform numerous experiments using data sets withtwo types of noise, insertion of noise features and blurring within-clustervalues of some features. These experiments allow us to conclude: (i) ouranomalous pattern initialisation method does indeed reduce the time ahierarchical clustering algorithm takes to complete, without negativelyimpacting its cluster recovery ability; (ii) A-Ward p\b{eta} provides bettercluster recovery than both Ward and Ward p.

A-Ward_p\b{eta}: Effective hierarchical clustering using the Minkowski metric and a fast k -means initialisation