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

problem is known as bisecting divisive clustering. Note that by recursively using a divisive bisecting clustering procedure, the dataset can be partitioned into any given number of clusters. Interestingly enough, the clusters so-obtained are structured as a hierarchical binary tree (or a binary taxonomy). This is the reason why the bisecting divisive approach is very attractive in many applications (e.g. in document-retrieval/indexing problems – see e.g. [17] and references cited therein). Among the divisive clustering algorithms which have been proposed in the literature in the last two decades ([13]), in this paper we will focus on two techniques: • the bisecting K-means algorithm; • the Principal Direction Divisive Partitioning (PDDP) algorithm.

On the performance of bisecting K-means and PDDP