Methodology for Emulating Self Organizing Maps for Visualization of Large Datasets

The self-organizing map (SOM) methodology does vector quantization and clustering on the dataset, and then projects these clusters in a lower dimensional space, such as 2D map, by positioning similar clusters in locations that are spatially closer in the lower dimension space. This makes the SOM methodology an effective tool for data visualization. However, in a world where mined information from big data have to be available immediately, SOM becomes an unattractive tool because of its space and time complexity. In this paper, we propose an alternative visualization methodology for large datasets with clustering information without the speed and memory constraints inherent to SOM. To demonstrate the efficiency and the vast potential of the proposed scheme as a fast visualization tool, the methodology is used to cluster and project the 3,823 image samples of handwritten digits of the Optical Recognition of Handwritten Digits dataset.