Abstract:
Cluster analysis is used to identify dissimilar
subgroups of objects out of a set of objects based on a
combination of rules. In the light of cluster analysis, it is possible
to treat dissimilar individuals in an appropriate manner by
taking their dissimilarity into consideration. This will be resulted
in enhancing the accuracy and efficiency of estimation and
prediction models. This study aims to evaluate the performance
of different partitioning methods namely, k-means, k-medoids
(PAM) and fuzzy and hierarchical methods namely,
agglomerative nesting and divisive analysis in grouping the
economic events affecting the foreign exchange market. Cluster
analysis performed on economic indicators data set depicts the
structure of clusters resulted from all algorithms are the same
except the single linkage of agglomerative nesting. Poor quality of
the clustering structure formed by the single linkage method is
confirmed by the lower value of average silhouette width.
Comparatively high value of agglomerative coefficient associated
with the ward’s method reveals the better performance of
clustering compared to other linkages. Economic indicators
under study are found to be clustered in three groups as
performing high, moderate and low impact on the movements of
exchange rates. High impact of economic indicators on the
exchange rates is reflected by the high volatility at release time
and shorter prevailing time of the impact after the release.
