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 an adaptive resonance theory predictive mapping (ARTMAP)model which uses incremental cluster validity indices (iCVIs) to performunsupervised learning, namely iCVI-ARTMAP. Incorporating iCVIs to thedecision-making and many-to-one mapping capabilities of ARTMAP can improve thechoices of clusters to which samples are incrementally assigned. Theseimprovements are accomplished by intelligently performing the operations ofswapping sample assignments between clusters, splitting and merging clusters,and caching the values of variables when iCVI values need to be recomputed.Using recursive formulations enables iCVI-ARTMAP to considerably reduce thecomputational burden associated with cluster validity index (CVI)-based offlineclustering. Depending on the iCVI and the data set, it can achieve runningtimes up to two orders of magnitude shorter than when using batch CVIcomputations. In this work, the incremental versions of Calinski-Harabasz,WB-index, Xie-Beni, Davies-Bouldin, Pakhira-Bandyopadhyay-Maulik, andnegentropy increment were integrated into fuzzy ARTMAP. Experimental resultsshow that, with proper choice of iCVI, iCVI-ARTMAP outperformed fuzzy adaptiveresonance theory (ART), dual vigilance fuzzy ART, kmeans, spectral clustering,Gaussian mixture models and hierarchical agglomerative clustering algorithms inmost of the synthetic benchmark data sets. It also performed competitively onreal world image benchmark data sets when clustering on projections and onlatent spaces generated by a deep clustering model. Naturally, the performanceof iCVI-ARTMAP is subject to the selected iCVI and its suitability to the dataat hand; fortunately, it is a general model wherein other iCVIs can be easilyembedded.

iCVI-ARTMAP: Accelerating and improving clustering using adaptive resonance theory predictive mapping and incremental cluster validity indices