• Seyfi M, Nayak R, Xu Y and Geva S. Efficient mining of discriminative itemsets. Proceedings of the International Conference on Web Intelligence. (451-459).

    https://doi.org/10.1145/3106426.3106429

  • Nofong V. EDTrend. Proceedings of the Australasian Computer Science Week Multiconference. (1-8).

    https://doi.org/10.1145/2843043.2843051

  • Yu K, Ding W, Simovici D and Wu X. Mining emerging patterns by streaming feature selection. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. (60-68).

    https://doi.org/10.1145/2339530.2339544

  • Kobyliński Ł and Walczak K. Efficient mining of jumping emerging patterns with occurrence counts for classification. Transactions on rough sets XIII. (73-88).

    /doi/10.5555/1985688.1985693

  • Lin Z, Jiang B, Pei J and Jiang D. (2010). Mining discriminative items in multiple data streams. World Wide Web. 13:4. (497-522). Online publication date: 1-Dec-2010.

    https://doi.org/10.1007/s11280-010-0094-0

  • Duan L, Zuo J, Zhang T, Peng J and Gong J. Mining contrast inequalities in numeric dataset. Proceedings of the 11th international conference on Web-age information management. (194-205).

    /doi/10.5555/1884017.1884044

  • Terlecki P. On the relation between jumping emerging patterns and rough set theory with application to data classification. Transactions on rough sets XII. (236-338).

    /doi/10.5555/1880429.1880442

  • Ramamohanarao K. Contrast pattern mining and its applications. Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104. (5-8).

    /doi/10.5555/1862242.1862245

  • Ceci M, Appice A and Malerba D. Emerging Pattern Based Classification in Relational Data Mining. Proceedings of the 19th international conference on Database and Expert Systems Applications. (283-296).

    https://doi.org/10.1007/978-3-540-85654-2_28

  • Terlecki P and Walczak K. (2007). Jumping emerging patterns with negation in transaction databases - Classification and discovery. Information Sciences: an International Journal. 177:24. (5675-5690). Online publication date: 20-Dec-2007.

    https://doi.org/10.1016/j.ins.2007.07.018

  • Ramamohanarao K and Fan H. (2007). Patterns Based Classifiers. World Wide Web. 10:1. (71-83). Online publication date: 1-Mar-2007.

    https://doi.org/10.1007/s11280-006-0012-7

  • Alhammady H and Ramamohanarao K. (2006). Using Emerging Patterns to Construct Weighted Decision Trees. IEEE Transactions on Knowledge and Data Engineering. 18:7. (865-876). Online publication date: 1-Jul-2006.

    https://doi.org/10.1109/TKDE.2006.116

  • Fan H and Ramamohanarao K. (2006). Fast Discovery and the Generalization of Strong Jumping Emerging Patterns for Building Compact and Accurate Classifiers. IEEE Transactions on Knowledge and Data Engineering. 18:6. (721-737). Online publication date: 1-Jun-2006.

    https://doi.org/10.1109/TKDE.2006.95

  • Alhammady H and Ramamohanarao K. Mining Emerging Patterns and Classification in Data Streams. Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence. (272-275).

    https://doi.org/10.1109/WI.2005.96

  • Fan H, Fan M and Wang B. Maximum item first pattern growth for mining frequent patterns. Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing. (515-523).

    /doi/10.5555/1783574.1783667

  • Fan H and Ramamohanarao K. A Bayesian approach to use emerging patterns for classification. Proceedings of the 14th Australasian database conference - Volume 17. (39-48).

    /doi/10.5555/820085.820096