Mnemonic SOMs : Recognizable Shapes for Self-Organizing Maps

The Self-Organizing Map (SOM) enjoys significant popularity in the field of data mining and visualization. While its topology-preserving mapping allows easier interpretation of complex data, communicating the location of clusters and individual data items as well as memorizing locations are not solved satisfactorily in conventional rectangular maps. In this paper, a variant of self-organizing maps following standard SOM training practices and having a regular grid structure, but in non-rectangular map shapes, is introduced. Utilizing different recognizable map shapes, such as country or continent maps, or geometrical shapes such as icons, easy description of the location of certain data items becomes possible, and provides an additional mnemonic clue for remembering the locations and relationships between clusters.