Developments on Solutions of the Normalized-Cut-Clustering Problem Without Eigenvectors

Lorente-Leyva, Leandro L.; Herrera-Granda, Israel David; Rosero-Montalvo, Paúl D.; Ponce-Guevara, K. L.; Castro-Ospina, Andrés Eduardo; Becerra, Miguel A.; Peluffo-Ordóñez, Diego H.; Rodríguez-Sotelo, José Luis

doi:10.1007/978-3-319-92537-0_37

Cited by 6 publications

(1 citation statement)

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“…Thus, a graph of nodes (representing data points) and an adjacency matrix (representing similarity between nodes) is constructed. Accordingly, the clustering procedure is carried out by partitioning the graph into disjoint subgraphs following a graph-based similarity criteria [50]. Normalized Cut Clustering (NCC) is a graph-based partitioning approach that uses distinct clustering criteria, initially proposed for image segmentation proposes [51].…”

Section: Proposed Semi Supervised Extensionmentioning

confidence: 99%

Semi Supervised NCC: Introducing Prior Knowledge To Normalized Cut Clustering

Aalaila,

Bamansour,

Elhamdi

et al. 2023

Preprint

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<p>Clustering plays a pivotal role in exploratory data analysis, especially in scenarios where prior knowledge about a subset of samples is available, prompting a rapid development of Semi-supervised clustering approaches. In this work, we introduce a novel method for seamlessly integrating prior knowledge into the widely recognized Normalized Cut Clustering (NCC) algorithm, thereby offering a natural extension within the realm of semi-supervised clustering. Similarly to NCC, the solution of the proposed method is spectral-based in the form of an inhomogeneous eigenvalue problem. Also, we show that the proposed method can be seen as a generalization of NCC, enhancing its applicability in scenarios where prior information is available. In addition, we design an adequate and comprehensive experimental setup. This setup not only tests the competitiveness of our approach but also showcases its superiority in terms of performance metrics.</p>

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Section: Proposed Semi Supervised Extensionmentioning

confidence: 99%