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 paper presents a thorough evaluation of the existing methods thataccelerate Lloyd's algorithm for fast k-means clustering. To do so, we analyzethe pruning mechanisms of existing methods, and summarize their common pipelineinto a unified evaluation framework UniK. UniK embraces a class of well-knownmethods and enables a fine-grained performance breakdown. Within UniK, wethoroughly evaluate the pros and cons of existing methods using multipleperformance metrics on a number of datasets. Furthermore, we derive anoptimized algorithm over UniK, which effectively hybridizes multiple existingmethods for more aggressive pruning. To take this further, we investigatewhether the most efficient method for a given clustering task can beautomatically selected by machine learning, to benefit practitioners andresearchers.

On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection