Abstract
Clustering and classification are among the most important problem tasks in the realm of data analysis, data mining and machine learning. In fact, while clustering can be seen as the most popular representative of unsupervised learning, classification (together with regression) is arguably the most frequently considered task in supervised learning. Even though the literature on clustering and classification abounds, the interest in these topics seems to be unwaning, both from a research and application point of view.
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Hüllermeier, E. (2010). Uncertainty in Clustering and Classification. In: Deshpande, A., Hunter, A. (eds) Scalable Uncertainty Management. SUM 2010. Lecture Notes in Computer Science(), vol 6379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15951-0_6
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DOI: https://doi.org/10.1007/978-3-642-15951-0_6
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