Spam Detection Based on a Hierarchical Self-Organizing Map

被引：0

作者：

Jose Palomo, Esteban ^{[1
]}

Dominguez, Enrique ^{[1
]}

Marcos Luque, Rafael ^{[1
]}

Munoz, Jose ^{[1
]}

机构：

[1] Univ Malaga, Dept Comp Sci, ETSI Informat, E-29071 Malaga, Spain

来源：

EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2009年 / 5755卷

关键词：

Data clustering; hierarchical self-organization; spam detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The GHSOM is an artificial neural network that has been widely used for data, clustering. The hierarchical architecture of the GHSOM is more flexible than a single SOM since it is adapted to input data, mirroring inherent; hierarchical relations among them. The adaptation process of the GHSOM architecture is controlled by two parameters. However, these parameters have to be established in advance and this task is not always easy. In this paper, a new hierarchical self-organizing model that has just one parameter is proposed. The performance of this model has been evaluated by building a spam detector. Experimental results confirm the goodness of this approach.

引用

页码：30 / 37

页数：8

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