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We cluster each symmetrized proximity matrix D sym t using the single-link criterion.
This can also be interpreted as a proximity matrix (Kruskal and Wish [21]).
Considering proximity to be undirected, we further symmetrize the proximity matrix to obtain D sym t.
The proximity matrix was used for identifying structure in the data (Breiman 2002).
A hierarchical algorithm can be viewed as a mapping of the data proximity matrix into a cophenetic matrix.
These samples are identified by creating a proximity matrix that calculates the similarity of each test sample to each training sample.
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GAP has a friendly graphical user interface for easy handling of data and proximity matrices.
They also present relevant concepts in this context, such as similarity (or proximity) matrices and measures.
It provides direct visual perception for exploring structures of a given data matrix and its corresponding proximity matrices, for variables and subjects.
We also divide our analysis into the biggest market sectors (Basic Materials, Consumer Goods, Financial, Health Care, Industrial Goods, Services, and Technology), where sector matrices are simply sub-matrices of the full proximity matrices composed of corporations from a single sector.
Clusters are first identified independently from the symmetrized proximity matrices of each time interval and secondly, the clustering output is smoothed by applying a temporal filter, so that clusters last for at least 10 s.
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