Self-Organizing Map for Clustering Algorithms in Programming Codes
Xingyin Zhu, Guojin Zhu · 2010
Self-organizing maps (SOMs), a data visualization technique invented by Professor Teuvo Kohonen, reduces the dimensions of data through the use of self-organizing neural networks. In this paper, we present an approach to cluster the different topics of knowledge from programming codes without manual labour. First, syntax trees are generated for programming codes, and then the similarities between them are computed in order to get a generalized mean of the syntax trees for the non-vectorial self-organizing maps model. On the visualization map, the different topics of knowledge extracted from the programming codes will be gathered together. The experiment will demonstrate its feasibility in the context of a algorithm clustering task.