References
- T. F. Liao, “Statistical Group Comparison”, Wiley's Series in Probability and Statistics, 2002.
- S. D. Bay, M. J. Pazzani, “Detecting Change in Categorical Data: Mining Contrast Sets”. In proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 15–18, 1999 San Diego, CA. 302–306.
- J. Lin, E. Keogh, “Group SAX: Extending the notion of contrast sets to time series and multimedia data. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-06), pages 284–296, 2006.
- aT. Menzies, Y. Hu, “Data Mining for Very Busy People”. IEEE Computer, October, 2003, pp. 18–25.
- P. K. Novak, N. Lavrac, G. I. Webb, “Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining”, Journal of Machine Learning Research, Vol. 10, Feb. 2009, pp. 377–403.
- S. D. Bay, “Multivariate Discretization of Continuous Variables for Set Mining”, In proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, MA. Aug 20–23, 2000.
- P. Kralj, N. Lavrac, D. Gamberger, A. Krstacic, “Contrast Set Mining through Subgroup Discovery Applied to Brain Ischaemic a Data”, PAKKD 2007.
- G. Dong and J. Li, “Efficient mining of emerging patterns: Discovering trends and differences”, In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), pages 43–52, 1999.
- S. Wrobel, “An algorithm for multi-relational discovery of subgroups”, In Proceedings of the 1st European Conference on Principles of Data Mining and Knowledge Discovery (PKDD-97), pages 78–87, 1997.
- R. J. Bayardo, “Efficiently Mining Long Patterns from Databases”, In Proceedings of the ACM International Conference on Management of Data (SIGMOD), 1988, pp. 85–93.
- G. I. Webb, S. Butler, D. Newlands, “On Detecting Differences between Groups”, In the Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, 2003, pp. 256–265, NY, USA.
- R. Agrawal, R. Srikantand, “Fast algorithms for mining association rules in large databases”, In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487–499, Santiago, Chile, September 1994.
- R. J. Hilderman, T. Pechham, “A Statistically Sound Alternative Approach to Mining Contrast Sets”, In Proceedings of the Australian Data Mining Conference, 2005, pp. 157–172, Australia.
- Z. He, X. Xu, S. Deng, “Mining Cluster-Defining Actionable Rules”, In Proceedings of the National Database Conference (NDBC), 2004.
- B. Minaei-Bidgoli, P-N. Tan, W. F. Punch, “Mining Interesting Contrast Rules for a Web-based Educational System”, in proceedings of International Conference on Machine Learning Application. Louisville, KY. Dec 1618, 2004.
- J. Lin and E. Keogh. “Group SAX: Extending the notion of contrast sets to time series and multimedia data”, In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-06), pages 284–296, 2006.
- G. Dong and J. Li, “Efficient mining of emerging patterns: Discovering trends and differences”, In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), pages 43–52, 1999.
- H. Fan and K. Ramamohanarao, “Efficiently mining interesting emerging patterns”, In Proceeding of the 4th International Conference on Web-Age Information Management (WAIM), pages 189–201, 2003.
- H. Fan, M. Fan, K. Ramamohanarao, M. Liu, “Further improving emerging pattern based classifiers via bagging”, In Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-06), pages 91–96, 2006.
- A. Soulet, B. Crmilleux, F. Rioult, “Condensed representation of emerging patterns”, In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 127–132, 2004.
- J. Li, G. Dong, and K. Ramamohanarao, “Instance-based classification by emerging patterns”, In Proceedings of the 14th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pages 191–200, 2000.
- J. Li, H. Liu, J. R. Downing, A. E-J. Yeoh, and L. Wong, “Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients”, Bioinformatics, vol. 19, no.l, pp. 71–78, 2003.
- H. S. Song, J. K. Kimb, S. H. Kima, “Mining the change of customer behavior in an internet shopping mail”, Expert Systems with Applications, vol. 21, no. 3, pp. 157–168, 2001.
- X. Ji, J. Bailey, G. Dong, “Mining minimal distinguishing sub-sequence patterns with gap constraints”, Knowledge Information Systems, vol. 11, no. 3, pp.259–286, 2007.
- K. Deng, O. Zaiane, “Contrasting Sequence Groups by Emerging Sequences”, Discovery Science, 2009.
- S. Chan, B. Kao, C. L. Yip, M. Tang, “Mining emerging sub-strings”, In Database Systems for Advanced Applications (DASFAA), page 119, 2003.
- D. Lo, H. Cheng, J. Han, S-C Khoo, “Classification of soft-ware behaviors for failure detection: A discriminative pattern mining approach”, In Knowledge Discovery and Data Mining Conference (KDD), 2009.
- S. Wrobel, “An algorithm for multi-relational discovery of subgroups”, In Proceedings of the 1st European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pp. 78–87, 1997.
- M. Atzmuller, F. Puppe, “SD-Map - a fast algorithm for exhaustive subgroup discovery”, In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pages 617, 2006.
- B. Kavsek, N. Lavrac, “APRIORI-SD: Adapting association rule learning to subgroup discovery”, Applied Artificial Intelligence, vol. 20, no. 7, pp. 543–583, 2006.
- S. Xiang, T. Yang, J. Ye, “Simultaneous feature and feature group selection through hard thresholding”, In proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 532–541, 2014.
- Z. Xu, G. Huang, K. Weinberger, A. Zheng, “Gradient boosted feature selection”, In proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 522–531, 2014.
- W. Klosgen, M. May, “Spatial subgroup mining integrated in an object-relational spatial database”, In Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 275–286, 2002.
- M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero, “Evolutionary fuzzy rule induction process for subgroup discovery: A case study in marketing”, IEEE Transactions on Fuzzy Systems, vol. 15, no. 4, pp. 578592, 2007.
- P. Kralj, N. Lavrac, D. Gamberger, A. Krstacic, “Contrast set mining through subgroup discovery applied to brain ischaemia data”, In Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, (PAKDD), pp. 579–586, 2007b.
- L. Langohr, V. Podpecan, M. petek, I. Mozeric, K. Gruden, N. Lavrac, H. Toivonen, “Contrast Subgroup Discovery”, The Computer Journal, 2012.
- I. Guyon, A. Elisseeff, “Overfitting in Making Comparisons Between Variable Selection Methods”, Journal of Machine Learning Research, vol. 3, pp. 13711382, 2003.
- Z. Al Aghbari, “Classification of Categorical and Numerical data on Selected Subset of Features”, Bayesian Networks, Sciyo Publisher, ISBN: 978-953-7619-X-X, Oct. 2010.
- Y. Liu, J. R. Kender, “Sort-Merge Feature Selection for Video Data”, SIAM Data Mining Conference (SDM), 2003, San Francisco, USA.
- Z. Al Aghbari, “Effective Image Mining by Representing Color Histograms as Time Series” the International Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 13, no.2, pp.109–114, 2009.
- I. N. Junejo, Z. Al Aghbari, “Using SAX Representation for Human Action Recognition” Elsevier International Journal of Visual Communication and Image Representation, Vol. 23, no. 6, 2012, pp. 853–861.
- H. Pang, S. L. George, K. Hui, T. Tong, “Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 5, pp. 1422–1431, 2012.
- X. Lina, F. Yanga, L. Zhoub, P. Yinb, H. Kongb, W. Xingc, X. Lub, L. Jiad, Q. Wanga, G. Xu, “A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information”, Elsevier Journal of Chromatography B, 2012.