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...

Full description

Bibliographic Details

Published in:	Information sciences Vol. 677; p. 120897
Main Authors:	Aghamolaei, Sepideh, Ghodsi, Mohammad
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.08.2024
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!