A new data topology matching technique with Multilevel Interior Growing Self-Organizing Maps

Self-Organizing Maps (SOM) are widely used for their ability to preserve the topology in the projection. However, this topology is not perfectly preserved due to the static structure of SOM. Therefore, we show in this paper a novel architecture of SOM which organizes itself over time. The proposed method called MIGSOM (Multilevel Interior Growing Self-Organizing Maps) is generated by a growth process which allows to adds nodes where it is necessary. The network start with a minimum number of nodes, then nodes will be added from the boundary as well as the interior of the network. The MIGSOM algorithm adds the interior nodes in a superior level of the map. As a result, the map can have three-Dimensional structure with multi-levels oriented maps. To improve the performance of the proposed algorithm, comparison of MIGSOM to the Kohonen feature Map (SOM) and the Growing Grid (GG) is made. Our experiment results demonstrate that the MIGSOM constructs better mappings than the classic SOM and GG, especially, in terms of data quantification and topology preservation.