Abstract
In this paper, we delve into the Mutual k-Nearest Neighbor Graph (\(m\)k\(NN G\)) and its significance in clustering and outlier detection. We present a rigorous mathematical framework elucidating its application and highlight its role in the success of various clustering algorithms. Building on Brito et al.’s findings, which link the connected components of the \(m\)k\(NN G\) to clusters under specific density bounds, we explore its relevance in the context of a wide range of density functions.
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This work is partly funded by the Swiss National Science Foundation under grant number 207509 “Structural Intrinsic Dimensionality”.
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Chavez, E., Marchand-Maillet, S., Quiroz, A.J. (2023). Mutual k-Nearest Neighbor Graph for Data Analysis: Application to Metric Space Clustering. In: Pedreira, O., Estivill-Castro, V. (eds) Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289. Springer, Cham. https://doi.org/10.1007/978-3-031-46994-7_3
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DOI: https://doi.org/10.1007/978-3-031-46994-7_3
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