Abstract
In this paper we present a graph-based clustering method particularly suited for dealing with data that do not come from a Gaussian or a spherical distribution. It can be used for detecting clusters of any size and shape, without the need of specifying neither the actual number of clusters nor other parameters.
The method has been tested on data coming from two different computer vision applications. A comparison with other three state-of-the-art algorithms was also provided, demonstrating the effectiveness of the proposed approach.
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Foggia, P., Percannella, G., Sansone, C., Vento, M. (2007). A Graph-Based Clustering Method and Its Applications. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_26
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DOI: https://doi.org/10.1007/978-3-540-75555-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75554-8
Online ISBN: 978-3-540-75555-5
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