Interpretability and refinement of clustering

Felix Iglesias Vazquez, Tanja Zseby, Arthur Zimek

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstrakt

The difficulty to validate clustering reliability hinders the adoption of clustering in real-life applications. We propose: (a) a set of symbolic representations to interpret problem spaces and (b) the CluReAL algorithm to refine any clustering result regardless of the used technique. Both approaches are grounded by recently published absolute cluster validity indices. Conducted experiments show how the refinement algorithm improves performances in a wide variety of scenarios and builds more interpretable solutions, whereas symbolic representations are shown to offer explainable summaries of problem contexts. Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in processes that depend on clustering.

OriginalsprogEngelsk
TitelProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020
RedaktørerGeoff Webb, Zhongfei Zhang, Vincent S. Tseng, Graham Williams, Michalis Vlachos, Longbing Cao
ForlagIEEE
Publikationsdato2020
Sider21-29
ISBN (Elektronisk)9781728182063
DOI
StatusUdgivet - 2020
Begivenhed7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 - Virtual, Sydney, Australien
Varighed: 6. okt. 20209. okt. 2020

Konference

Konference7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020
Land/OmrådeAustralien
ByVirtual, Sydney
Periode06/10/202009/10/2020
SponsorACM, American Statistical Association (ASA), et al., IEEE, IEEE Computational Intelligence Society, KDD

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