Title :
A new outlier detection base on the self-organizing map algorithm
Author :
Feng Xiangdong ; Li Yueshan ; Ma Zhiyuan
Author_Institution :
Eng. & Tech. Coll., Chengdu Univ. of Technol., Leshan, China
Abstract :
This article applies the method of artificial neural networks in the outlier detection, and gives one kind of new outlier detection method. According to the thoughts of the GHSOM algorithm and the GHTSOM algorithm, we make the improvement to the SOM algorithm. The improvement algorithm can be applied in the outlier detection, and this dissertation gives the different outlier detection examples and analyze the algorithm performance and the expansion ability, the performance is quite stable and adaptation is quite strong to the different date. Compares with the outlier detection use support vector machines, this method doesn´t need to choose the kernel function and adjust the parameter unceasingly, and it has very good adaptation to the change of the data distribution.
Keywords :
learning (artificial intelligence); self-organising feature maps; support vector machines; GHSOM algorithm; GHTSOM algorithm; artificial neural networks; outlier detection method; self-organizing map algorithm; support vector machines; Abstracts; Artificial neural networks; Educational institutions; Electronic mail; Kernel; Support vector machines; Growing hierarchical self-organizing map algorithm; Growing hierarchical tree self-organizing map algorithm; Outlier detection; Self-organizing map algorithm;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an