S. Kotsiantis, D. Kanellopoulos, Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)
P. Lenca, P. Meyer, B. Vaillant, S. Lallich, On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. Eur. J. Oper. Res. 184(2), 610–626 (2008)
K. Techapichetvanich, A. Datta, Visual mining of market basket association rules, in Computational Science and Its Applications-ICCSA, 2004 (Springer, Berlin, 2004), pp. 479–488
M. Aleksandrova, A. Brun, O. Chertov, A. Boyer, Sets of contrasting rules to identify trigger factors, in ECAI 2016: 22nd European Conference on Artificial Intelligence (IOS Press, 2016)
M. Aleksandrova, O. Chertov, A. Brun, A. Boyer, Contrast classification rules for mining local differences in medical data, in 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol. 2, (IEEE, 2017), pp. 880–883
M. Aleksandrova, A. Brun, O. Chertov, A. Boyer, Sets of contrasting rules: a supervised descriptive rule induction pattern for identification of trigger factors, in 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) (IEEE, 2016) pp. 431–435
R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)
B. Liu, W. Hsu, M. Yiming, Integrating classification and association rule mining, in Proceedings of the fourth international conference on knowledge discovery and data mining (1998), pp. 80–86
R. Agrawal, R. Srikant et al., Fast algorithms for mining association rules, in Proceedings 20th International Conference very large data bases, VLDB, vol. 1215 (1994), pp. 487–499
K. Ramamohanarao, J. Bailey, H. Fan, Efficient mining of contrast patterns and their applications to classification, in 2005 3rd International Conference on Intelligent Sensing and Information Processing (IEEE, 2005), pp. 39–47
P.K. Novak, N. Lavrač, G.I. Webb, Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)
G. Dong, J. Li, Efficient mining of emerging patterns: discovering trends and differences, in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. (ACM, 1999), pp. 43–52
G.I. Webb, S. Butler, D. Newlands, On detecting differences between groups, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2003), pp. 256–265
O. Chertov, M. Aleksandrova, Fuzzy clustering with prototype extraction for census data analysis, in Soft Computing: State of the Art Theory and Novel Applications (Springer, Berlin, 2013), pp. 289–313
M. Girotra, K. Nagpal, S. Minocha, N. Sharma, Comparative survey on association rule mining algorithms. Int. J. Comput. Appl. 84(10), (2013)
J. Hipp, U. Güntzer, G. Nakhaeizadeh, Algorithms for association rule mining a general survey and comparison. ACM Sigkdd Explor. Newsl. 2(1), 58–64 (2000)
C.C. Aggarwal, M.A. Bhuiyan, M. Al Hasan, Frequent pattern mining algorithms: a survey, in Frequent pattern mining (Springer, Berlin, 2014), pp. 19–64
M.J. Zaki, S. Parthasarathy, M. Ogihara, W. Li et al., New algorithms for fast discovery of association rules, in KDD, vol. 97 (1997), pp. 283–286
M.J. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)
