Abstract

SelfOrganizing Maps (SOMs) are a form of Machine Learning methods which are popularly applied as a tool to either cluster vectorial information, or to produce a topology preserving projection of high dimensional data vectors onto a low dimensional (often 2dimensional) display space [20]. A SOM is generally trained unsupervised. The computational complexity of the underlying algorithms grows linearly with the size and number of inputs, which renders the SOM suitable for data mining tasks. The standard SOM algorithm is defined on input domains involving fixed sized data vectors. It is however recognized that many problem domains are naturally represented by structured data which are more complex than fixed sized vectors. Just to give some examples, in speech recognition, data is available in the form of variable length temporal vectors, while in Chemistry data is most naturally represented through molecular graphs.Moreover, numerous data mining tasks provide structural information which may be important to consider during the processing. For example, document mining in the world wide web involves both interdocument structure due to the formatting or hypertext structure, and intradocument structure due to hyperlink or reference dependencies. Note that any model capable of dealing with graphs can be used also in applications involving vectors, sequences, and trees, since these are special cases of graphs. �� 2009 SpringerVerlag Berlin Heidelberg.