Title :
Self-Organizing Map for Clustering Algorithms in Programming Codes
Author :
Zhu, Xingyin ; Zhu, Guojin
Author_Institution :
Sch. of Comput. Sci. & Technol., Donghua Univ., Shanghai, China
Abstract :
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.
Keywords :
codes; computational linguistics; data handling; data visualisation; pattern clustering; self-organising feature maps; tree data structures; clustering algorithm; data visualization technique; manual labour; nonvectorial self organizing map; programming code; self organizing neural network; syntax tree; visualization map; Clustering algorithms; Data visualization; Programming; Proteins; Self organizing feature maps; Syntactics; algorithm clustering; non-vectorial; self-organizing map; syntax tree;
Conference_Titel :
Business Intelligence and Financial Engineering (BIFE), 2010 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7575-9
DOI :
10.1109/BIFE.2010.16
