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Cluster Analysis

Cluster Analysis helps risk managers in identifying different groups (clusters) in a given portfolio data.

What is Cluster Analysis?

Cluster Analysis helps risk managers in identifying different groups (clusters) in a given portfolio data. It assists in the identification of groups of similar cases in a data set. Groups represent observation subsets that exhibit homogeneity (i.e., similarities) due to variables’ profiles that allow them to be distinguished from those found in other groups. In the context of a database with variables in columns and observations in rows, cluster analysis serves to aggregate borrowers based on the profile of their variables.

Example of Cluster Analysis?

Broadly, there are two approaches that can be used to implement cluster analysis including hierarchical/aggregative clustering and divisive/partitioned clustering.
In hierarchical clustering, we build cluster hierarchies and aggregate them on a case-by-case basis to form a tree structure with the clusters shown as leaves and the whole population shown as the roots. Clusters are merged together beginning at the leaves, and branches are followed until arriving at the roots.

Why is Cluster Analysis important?

One critical aspect of default predictive modelling is to identify the number of groups from the given portfolio to build a separate model as every group has its own distinct properties.

Owais Siddiqui
1 min read
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