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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2021

19.11.2020 | Original Article

A robust spectral clustering algorithm based on grid-partition and decision-graph

verfasst von: Lijuan Wang, Shifei Ding, Yanru Wang, Ling Ding

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2021

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Abstract

Spectral clustering (SC) transforms the dataset into a graph structure, and then finds the optimal subgraph by the way of graph-partition to complete the clustering. However, SC algorithm constructs the similarity matrix and feature decomposition for overall datasets, which needs high consumption. Secondly, k-means is taken at the clustering stage and it selects the initial cluster centers randomly, which leads to the unstable performance. Thirdly, SC needs prior knowledge to determine the number of clusters. To deal with these issues, we propose a robust spectral clustering algorithm based on grid-partition and decision-graph (PRSC) to reduce the amount of calculation and improve the clustering efficiency. In addition, a decision-graph method is added to identify the cluster centers quickly to improve the algorithm stability without any prior knowledge. A numerical experiments validate that PRSC algorithm can effectively improve the efficiency of SC. It can quickly obtain the stable performance without any prior knowledge.
Vorheriger Artikel MAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA
Nächster Artikel Classifying imbalanced data using SMOTE based class-specific kernelized ELM

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Metadaten
Titel
A robust spectral clustering algorithm based on grid-partition and decision-graph
verfasst von
Lijuan Wang
Shifei Ding
Yanru Wang
Ling Ding
Publikationsdatum
19.11.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01231-2

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