Explainable graph clustering via expanders in the massively parallel computation model

Explainable clustering provides human-understandable reasons for decisions in black-box learning models. In a previous work, a decision tree built on the set of dimensions was used to define ranges of values for k-means clusters. For explainable graph clustering, we use expander graphs instead of de...

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Vydané v:	Information sciences Ročník 677; s. 120897
Hlavní autori:	Aghamolaei, Sepideh, Ghodsi, Mohammad
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.08.2024
Predmet:	DBSCAN Expander graphs Explainable clustering Fixed-parameter algorithms Massively parallel computation Expander graphs DBSCAN Explainable clustering Fixed-parameter algorithms Massively parallel computation
ISSN:	0020-0255
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