Self organizing maps for the clustering of large sets of labeled graphs

Data mining on Web documents is one of the most challenging tasks in machine learning due to the large number of documents on the Web, the underlying structures (as one document may refer to another document), and the data is commonly not labeled (the class in which the document belongs is not known a-priori). This paper considers latest developments in Self-Organizing Maps (SOM), a machine learning approach, as one way to classifying documents on the Web. The most recent development is called a Probability Mapping Graph Self-Organizing Map (PMGraphSOM), and is an extension of an earlier Graph-SOM approach; this encodes undirected and cyclic graphs in a scalable fashion. This paper illustrates empirically the advantages of the PMGraphSOM versus the original GraphSOM model in a data mining application involving graph structured information. It will be shown that the performances achieved can exceed the current state-of-the art techniques on a given benchmark problem.