Abstract
The goal of this chapter is to survey and present the main concepts and techniques from the vast collection of clustering models, from the hierarchical to the partitioning ones. In the partitioning class, we extensively discuss the clustering approaches that can be seen as particular neural networks, with attention to the key concepts involved into the social sciences’ applications. The clustering is one of the main ingredients of the new copula-based algorithm of aggregation which will be discussed in Part II.
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Notes
- 1.
Divisive methods do the opposite of the agglomerative ones.
- 2.
This kind of model is very familiar to physicists, and they represent the vigor of the shaking as a temperature because at higher temperature, there is more molecular activity. Annealing refers to a way of tempering certain alloys of metal by heating and then gradually cooling them.
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Bernardi, E., Romagnoli, S. (2021). Clustering. In: Counting Statistics for Dependent Random Events. Springer, Cham. https://doi.org/10.1007/978-3-030-64250-1_1
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