| Title: |
Email Mining: Emerging Techniques for Email Management |
| Author(s): |
I. Katakis, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file.
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| Keywords: |
email, e-mail, mining, text, data streams, email classification, email clustering, automatic answering, spam filtering, text mi ning.
|
| Appeared in: |
Web Data Management Practices: Emerging Techniques and Technologies, Athena Vakali, George Pallis (Ed.), Idea Group Publishing, pp. 219-240, 2006.
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| Abstract: |
Email has met tremendous popularity over the past few years. People are sending and receiving many messages per day, communicating with partners and friends, or exchanging files and information. Unfortunately, the phenomenon of email overload has grown over the past years becoming a personal headache for users and a financial issue for companies. In this chapter, we will discuss how disciplines like Machine Learning and Data Mining can contribute to the solution of the problem by constructing intelligent techniques which automate email managing tasks and what advantages they hold over other conventional solutions. We will also discuss the particularity of email data and what special treatment it requires. Some interesting email mining applications like mail categorization, summarization, automatic answering and spam filtering will be also presented. |
| See also : |
Book Page at the IDEA Group Web Site
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This paper has been cited by the following:
| 1 |
Xie De-Ping. "A Study Report for Mining Email", Journal of Software, 2006,15(9)1200-1210. |
| 2 |
X. Zhang, J. Liu, Y. Zhang, C. Wang. "Spam behavior recognition based on session layer data mining", Proc. 3rd Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD 2006), LNAI 4223, pp 1289-1298, 2006.
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| 3 |
G. Cselle. "Organizing Email", MSc Thesis, Department of Computer Science and Electrical Engineering, ETH Zurich, Switzerland, 2006. |
| 4 |
Berendt, B. and Draheim, M. (2007). The Image of Germany in the World: An Email and Web Mining Approach. Künstliche Intelligenz, 3/07, 30-36. |
| 5 |
Li, C., Liu, J. (2009) Combining behavior and Bayesian Chinese spam filter, Proceedings of 2009 IEEE International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC2009, art. no. 5360937, pp. 389-392.
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| 6 |
Sneiders, E. (2010) Automated Email Answering by Text Pattern Matching, Advances in Natural Language Processing, Lecture Notes in Computer Science, 2010, Volume 6233/2010, 381-392
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| 7 |
Nagwani, N.K., Bhansali, A. (2010) An Email Clustering Model Using Weighted Similarities between Emails Attributes, International Journal of Research and Reviews in Computer Science 1(2), June 2010, pp. 1-6.
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| 8 |
Bekhuis,T.;Kreinacke,M.;Spallek,H.;Song,M.;O'Donnell,J.A. (2011) Using Natural Language Processing to Enable In-depth Analysis of Clinical Messages Posted to an Internet Mailing List: A Feasibility Study, J Med Internet Res. 2011 Oct-Dec; 13(4): e98.
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| 9 |
Lalla,H. (2011) E-mail Forensic Authorship Attribution, MSc Thesis, University of Fort Hare, South Africa
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| 10 |
Marin,A. (2011) Comparison of automatic classifiers' performances using word-based feature extraction techniques in an e-government setting, Kungliga Tekniska Högskolan, Stockholm
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| 11 |
Khan,S.R.;Nirkhi,S.M.;Dharaskar,R.V. (2012) Mining E-mail Content for Cyber Forensic Investigation, UACEE International Journal of Computer Science and its Applications - Volume 2: Issue 2, Page No : 112 - 116 |
| 12 |
Bogawar,P.S.; Bhoyar,K.K. (2012) Email Mining: A Survey, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 1, January 2012 ISSN (Online): 1694-0814
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