Title :
A batch version of the SOM for symbolic data
Author :
Chen, De-Hua ; Hung, Wen-Liang ; Yang, Miin-Shen
Author_Institution :
Inst. of Stat. Sci., Acad. Sinica, Taipei, Taiwan
Abstract :
Kohonen´s self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. In general, the SOM neural network is constructed as a learning algorithm for numerical data. However, except these numeric data, there are many other data types such as symbolic data. Thus, Yang et al. proposed a new SOM algorithm to treat symbolic data. In order to speed up the learning efficiency, in this paper we are interested in considering a batch learning SOM to treat symbolic data. Therefore, a new batch SOM algorithm, called a batch symbolic SOM (BS-SOM), is proposed to deal with symbolic data. Finally, we apply the BS-SOM to some real examples. The results show feasibility of our BS-SOM in real applications.
Keywords :
data handling; learning (artificial intelligence); self-organising feature maps; Kohonen self-organizing map; SOM neural network; batch learning SOM; batch symbolic SOM; batch version; competitive learning neural network; learning algorithm; neighborhood lateral interaction function; symbolic data; topological structure; Algorithm design and analysis; Artificial neural networks; Biological neural networks; Clustering algorithms; Image color analysis; Neurons; TV; batch learning; classification; neural network; self-organizing map(SOM); symbolic data;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583160
