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

Cluster analysis of very high dimensional data can benefit from theproperties of such high dimensionality. Informally expressed, in this work, ourfocus is on the analogous situation when the dimensionality is moderate tosmall, relative to a massively sized set of observations. Mathematicallyexpressed, these are the dual spaces of observations and attributes. The pointcloud of observations is in attribute space, and the point cloud of attributesis in observation space. In this paper, we begin by summarizing variousperspectives related to methodologies that are used in multivariate analytics.We draw on these to establish an efficient clustering processing pipeline, bothpartitioning and hierarchical clustering.

Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds