A maximal-clique-based clustering approach for multi-observer multi-view data by using k-nearest neighbor with S-pseudo-ultrametric induced by a fuzzy similarity

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A maximal-clique-based clustering approach for multi-observer multi-view data by using k-nearest neighbor with S-pseudo-ultrametric induced by a fuzzy similarity

Partitioning multi-view data is a recent challenge in clustering methods, which traditionally consider single-view data. In clustering techniques, finding the similarity or distance between objects, handled by metrics in Rn, plays a central role in community detection. Under this framework, differen...

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Bibliographic Details
Main Authors:	Khameneh, Azadeh Zahedi, Ghaznavi, Mehrdad, Kilicman, Adem, Mahad, Zahari, Mardani, Abbas
Format:	Article
Published:	Springer Science and Business Media Deutschland GmbH 2024
Online Access:	http://psasir.upm.edu.my/id/eprint/112041/

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