Self-Organizing Map for Clustering Algorithms in Programming Codes

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...

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Bibliographic Details
Published in:2010 Third International Conference on Business Intelligence and Financial Engineering pp. 24 - 27
Main Authors: Xingyin Zhu, Guojin Zhu
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2010
Subjects:
algorithm clustering
Clustering algorithms
Data visualization
non-vectorial
Programming
Proteins
Self organizing feature maps
self-organizing map
Syntactics
syntax tree
ISBN:1424475759, 9781424475759
Online Access:Get full text
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Summary: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.
ISBN:1424475759
9781424475759
DOI:10.1109/BIFE.2010.16