Abstract
This chapter reviews statistical approaches to the clustering problem, i.e. the task of partitioning data-sets in classes in such a way that points in the same class are more similar to one another than to those in other classes. Although this is technically an ill-posed problem, it is of great importance in a wide range of applications and numerous methods have been proposed to tackle it. This paper reviews mainly the coupled maps approach to clustering which performs a non-parametric classification without any assumptions on the distribution of clusters or the number of classes. The technique is illustrated on a biological example (reconstruction of phylogenetic trees) and one from coding theory. The merits of various approaches and the remaining challenges are also discussed.
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Stramaglia, S., Angelini, L., Marangi, C., Nitti, L., Pellicoro, M. (2004). Statistical Physics and the Clustering Problem. In: Wille, L.T. (eds) New Directions in Statistical Physics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-08968-2_15
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DOI: https://doi.org/10.1007/978-3-662-08968-2_15
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