Cluster Evolution Analysis: Identification and detection of similar clusters and migration patterns

Abstract

Cluster analysis often addresses a specific point in time, ignoring previous cluster analysis products. The present study proposes a model entitled Cluster Evolution Analysis (CEA) that addresses three phenomena likely to occur over time: 1) changes in the number of clusters; 2) changes in cluster characteristics; 3) between-cluster migration of objects.

To achieve this goal, two new techniques are implemented: To find similarities between clusters at different points in time, we used the moving average of cluster centroid technique, and to detect prominent migration patterns we used the clustering of clusters technique. The research introduces two new visual tools displaying all the clusters over the entire time period under study in a single graph.

The model was tested on five-year trade data of corporate bonds (2010-2014). The results obtained by the CEA model were checked and validated against the bond rating report issued periodically by the local bond rating company.

The results proved the model capable of identifying repeated clusters at various points in time, and detecting patterns that predict prospective loss of value, as well as patterns that indicate stability and preservation of value over time