R. Agrawal, T. Imieli-'nski, and A. Swami, Mining association rules between sets of items in large databases, ACM SIGMOD Record, vol.22, issue.2, pp.207-216, 1993.
DOI : 10.1145/170036.170072

S. Argamon and S. Levitan, Measuring the usefulness of function words for authorship attribution, Proceedings of the Joint Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing, 2005.

H. Baayen, H. Van-halteren, A. Neijt, and F. Tweedie, An experiment in authorship attribution, 6th JADT, pp.29-37, 2002.

J. Burrows, Delta " : A measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing, pp.267-287, 2002.

C. Chung and J. W. Pennebaker, The psychological functions of function words, pp.343-359, 2007.

D. Roeck, A. Sarkar, A. Garthwaite, and P. , Frequent Term Distribution Measures for Dataset Profiling, LREC, 2004.

J. Diederich, J. Kindermann, E. Leopold, and G. Paass, Authorship attribution with support vector machines, Applied Intelligence, vol.19, issue.1/2, pp.109-123, 2003.
DOI : 10.1023/A:1023824908771

P. Fournier-viger and V. S. Tseng, Mining Top-K Sequential Rules, Advanced Data Mining and Applications, pp.180-194, 2011.
DOI : 10.1016/S0306-4379(03)00072-3

M. Gamon, Linguistic correlates of style, Proceedings of the 20th international conference on Computational Linguistics , COLING '04, p.611, 2004.
DOI : 10.3115/1220355.1220443

D. I. Holmes, M. Robertson, and R. Paez, Stephen Crane and the New-York Tribune: A case study in traditional and non-traditional authorship attribution, Computers and the Humanities, vol.35, issue.3, pp.315-331, 2001.
DOI : 10.1023/A:1017549100097

D. L. Hoover, Frequent Collocations and Authorial Style, Literary and Linguistic Computing, pp.261-286, 2003.
DOI : 10.1093/llc/18.3.261

URL : http://llc.oxfordjournals.org/cgi/content/short/18/3/261

V. Ke?elj, F. Peng, N. Cercone, T. , and C. , N-gram-based author profiles for authorship attribution, Proceedings of the conference pacific association for computational linguistics, pp.255-264, 2003.

D. V. Khmelev and F. J. Tweedie, Using Markov chains for identification of writers, Literary and Linguistic Computing, pp.299-307, 2001.
DOI : 10.1093/llc/16.3.299

M. Koppel and J. Schler, Authorship verification as a one-class classification problem, Twenty-first international conference on Machine learning , ICML '04, p.62, 2004.
DOI : 10.1145/1015330.1015448

O. V. Kukushkina, A. A. Polikarpov, and D. V. Khmelev, Using literal and grammatical statistics for authorship attribution, Problems of Information Transmission, vol.37, issue.2, pp.172-184, 2001.
DOI : 10.1023/A:1010478226705

C. H. Ramyaa, R. , and K. , Using machine learning techniques for stylometry, Proceedings of International Conference on Machine Learning, 2004.

J. Rudman, The state of authorship attribution studies: Some problems and solutions, Computers and the Humanities, vol.31, issue.4, pp.351-365, 1997.
DOI : 10.1023/A:1001018624850

F. Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys, vol.34, issue.1, pp.1-47, 2002.
DOI : 10.1145/505282.505283

E. Stamatatos, A survey of modern authorship attribution methods, Journal of the American Society for Information Science and Technology, vol.57, issue.3, pp.538-556, 2009.
DOI : 10.1002/asi.21001

E. Stamatatos, N. Fakotakis, and G. Kokkinakis, Computer-based authorship attribution without lexical measures, Computers and the Humanities, vol.35, issue.2, pp.193-214, 2001.
DOI : 10.1023/A:1002681919510

G. U. Yule, The statistical study of literary vocabulary. CUP Archive, 1944.

Y. Zhao and J. Zobel, Effective and Scalable Authorship Attribution Using Function Words, Information Retrieval Technology, pp.174-189, 2005.
DOI : 10.1007/11562382_14