  The performance of the Self-Organizing Map (SOM) algorithm is dependent on
the initial weights of the map. The different initialization methods can
broadly be classified into random and data analysis based initialization
approach. In this paper, the performance of random initialization (RI) approach
is compared to that of principal component initialization (PCI) in which the
initial map weights are chosen from the space of the principal component.
Performance is evaluated by the fraction of variance unexplained (FVU).
Datasets were classified into quasi-linear and non-linear and it was observed
that RI performed better for non-linear datasets; however the performance of
PCI approach remains inconclusive for quasi-linear datasets.
