DocumentCode :
2828533
Title :
Improved SOM algorithm-EDSOM applied in text clustering
Author :
Ai-xiang Sun ; Xiu-yan Yu
Author_Institution :
Manage. Inst., Shandong Univ. of Technol., Zibo, China
Volume :
6
fYear :
2010
fDate :
22-24 Oct. 2010
Abstract :
SOM neural network is one of the most commonly used clustering algorithom in the text clustering field, but the learning strategies of SOM Network Clustering does not pursue the goal of the text clustering - the smallest deviation of the clusters, so it is very difficult to get the clusters with the smallest deviation. According to the principle of equal deviation, this paper presents an improved learning strategy: introduce equal cluster deviation theory into the learning process of SOM neural network, guide SOM neural network learning through adjusting the cluster deviation to be equal, with an expect to get clusters with the smallest deviation. The experimental results show that: by the measurement of the average accuracy, the algorithm shows a good performance.
Keywords :
learning (artificial intelligence); pattern clustering; self-organising feature maps; text analysis; SOM algorithm-EDSOM; SOM neural network learning; equal cluster deviation theory; text clustering; Neural networks; Sun; SOM neural network; average accuracy; equal deviation; text clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
Type :
conf
DOI :
10.1109/ICCASM.2010.5620096
Filename :
5620096
Link To Document :
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