In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2].

本文提出了一种基于集群分类技术（CCM）的电子邮件作者鉴定（EAI）模型，扩展了传统的特征集以包括一些更有趣和有效的特征，如邮件中使用的最后标点符号，作者开始邮件时使用大写字母的倾向以及问候语或告别语后的标点符号。文章通过实验结果表明，所提出的CCM模型以及所提出的特征集在作者鉴定任务准确性方面表现优异，超过了支持向量机（SVM）基于模型以及Iqbal等人提出的模型。

基于CCM的电子邮件作者识别模型