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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 2/2023

27.11.2022 | Short Paper

Consensus similarity graph construction for clustering

verfasst von: Tülin İnkaya

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2023

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Abstract

A similarity graph represents the local characteristics of a data set, and it is used as input to various clustering methods including spectral, graph-based, and hierarchical clustering. Several similarity graphs exist in the literature; however, there is not a single similarity graph that can handle all kinds of cluster shapes and structures. In this study, motivated by the successful applications of ensemble approaches to clustering, a generic method for consensus similarity graph construction is proposed. The proposed approach first constructs multiple similarity graphs using bootstrap aggregating (bagging). Then, these graphs are fused into a consensus similarity graph using the normalized co-association matrix. We use k-nearest neighbor, \(\varepsilon\)-neighborhood, fully connected graph, and proximity graphs as the base similarity graphs. Moreover, the proposed approach is coupled with various clustering algorithms including spectral, graph-based, and hierarchical clustering. The experimental results with various spatial and real data sets demonstrate the effectiveness of the consensus similarity graphs in clustering. The proposed approach is also robust to local noise.
Vorheriger Artikel Event prediction with rough-fuzzy sets
Nächster Artikel Instance hardness and multivariate Gaussian distribution-based oversampling technique for imbalance classification

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Metadaten
Titel
Consensus similarity graph construction for clustering
verfasst von
Tülin İnkaya
Publikationsdatum
27.11.2022
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-022-01116-w

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