Abstract
Nowadays emails have been an easy and fast tool of communication among people. As a result, filtering unsolicited/spam emails has become a very important challenge to achieve. Recently there has been some research work in text mining that combines text clustering with classification to improve the classification performance. In this paper, we investigate the effect of combining text clustering using K-means algorithm with various supervised classification mechanisms on improving the performance of classification of emails into spam or non-spam. The conjunction of clustering and classification mechanisms is carried out by adding extra features from the clustering step to the feature space used for classification. Our results show that combining K-means clustering with supervised classification by this methodology does not always improve the classification performance. Moreover, for the cases that the classifiers performance is improved by clustering, we found that the performance of classifiers in terms of accuracy is slightly increased with a very small amount that does not meet the increase in the time taken for building a learning model that combines both mechanisms. The result of our experiment has been shown using the Enron-Spam datasets.
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Weiss, S.M., et al.: Overview of text mining. In: Weiss, S.M., Indurkhya, N., Zhang, T. (eds.) Fundamentals of Predictive Text Mining, Chap. 1, pp. 1–12. Springer, London (2010)
Hamerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM) (2002)
Sasaki, M., Shinnou, H.: Spam detection using text clustering. In: Proceedings of the 2005 International Conference on Cyberworlds (CW 2005), pp. 316–319 (2005)
Kyriakopoulou, A., Kalamboukis, T.: Using clustering to enhance text classification. In: Proceedings of SIGIR 2007, Amsterdam, The Netherlands (2007)
Kyriakopoulou, A., Kalamboukis, T.: Combining clustering with classification for spam detection in social bookmarking systems. In: ECML/PKDD Discovery Challenge (2008)
Tretyakov, K.: Machine learning techniques in spam filtering. In: Data Mining Problem-oriented Seminar, Institute of Computer Science, University of Tartu, pp. 60–79 (2004)
Basavaraju, M., Prabhakar, R.: A novel method of spam mail detection using text based clustering approach. Int. J. Comput. Appl. 5(4), 15–25 (2010)
Alsmadi, I., Alhami, I.: Clustering and classification of email contents. J. King Saud Univ. Comput. Inf. Sci. 27(1), 46–57 (2015)
Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30115-8_22
Elssied, N.O.F., Ibrahim, O., Abu-Ulbeh, W.: An improved of spam e-mail classification mechanism using k-means clustering. J. Theor. Appl. Inf. Technol. 60(3), 568–580 (2014)
Set, Spam Base Data. https://archive.ics.uci.edu/ml/datasets/Spambase
Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. J. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley, Boston (2005)
Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentice-Hall Inc., Upper Saddle River (1992)
Text categorization with WEKA. https://weka.wikispaces.com/Text+categorization+with+WEKA
Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., Scuse, D.: WEKA Manual for Version 3-6-8. University of Waikato, Hamilton, New Zealand (2012)
Hidalgo, J.M.G.: Text mining in WEKA: chaining filters and classifiers, January 2013
Sarukkai, R.R.: Foundations of Web Technology. The Springer International Series in Engineering and Computer Science, vol. 698. Springer, Heidelberg (2002)
Teknomo, K.: K-means clustering tutorial. http://people.revoledu.com/kardi/tutorial/kMean/index.html. Accessed July 2007
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Hassan, D. (2017). Investigating the Effect of Combining Text Clustering with Classification on Improving Spam Email Detection. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_10
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DOI: https://doi.org/10.1007/978-3-319-53480-0_10
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