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

This study focuses on the optimization of the Big-means algorithm forclustering large-scale datasets, exploring four distinct parallelizationstrategies. We conducted extensive experiments to assess the computationalefficiency, scalability, and clustering performance of each approach, revealingtheir benefits and limitations. The paper also delves into the trade-offsbetween computational efficiency and clustering quality, examining the impactsof various factors. Our insights provide practical guidance on selecting thebest parallelization strategy based on available resources and datasetcharacteristics, contributing to a deeper understanding of parallelizationtechniques for the Big-means algorithm.

Strategies for Parallelizing the Big-Means Algorithm: A Comprehensive Tutorial for Effective Big Data Clustering