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Applied Computational Intelligence and Soft Computing
Volume 2011 (2011), Article ID 416308, 8 pages
Research Article

Classification of Textual E-Mail Spam Using Data Mining Techniques

Institute of Information Technology of Azerbaijan National Academy of Sciences, 9 F. Agayev Street, Baku 1141, Azerbaijan

Received 19 May 2011; Revised 23 August 2011; Accepted 5 September 2011

Academic Editor: Sebastian Ventura

Copyright © 2011 Rasim M. Alguliev et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new method for clustering of spam messages collected in bases of antispam system is offered. The genetic algorithm is developed for solving clustering problems. The objective function is a maximization of similarity between messages in clusters, which is defined by 𝑘-nearest neighbor algorithm. Application of genetic algorithm for solving constrained problems faces the problem of constant support of chromosomes which reduces convergence process. Therefore, for acceleration of convergence of genetic algorithm, a penalty function that prevents occurrence of infeasible chromosomes at ranging of values of function of fitness is used. After classification, knowledge extraction is applied in order to get information about classes. Multidocument summarization method is used to get the information portrait of each cluster of spam messages. Classifying and parametrizing spam templates, it will be also possible to define the thematic dependence from geographical dependence (e.g., what subjects prevail in spam messages sent from certain countries). Thus, the offered system will be capable to reveal purposeful information attacks if those occur. Analyzing origins of the spam messages from collection, it is possible to define and solve the organized social networks of spammers.