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The performance of the Self-Organizing Map (SOM) algorithm is dependent onthe initial weights of the map. The different initialization methods canbroadly be classified into random and data analysis based initializationapproach. In this paper, the performance of random initialization (RI) approachis compared to that of principal component initialization (PCI) in which theinitial 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 observedthat RI performed better for non-linear datasets; however the performance ofPCI approach remains inconclusive for quasi-linear datasets.

Initialization of Self-Organizing Maps: Principal Components Versus Random Initialization. A Case Study